Triple
T10341897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Visa Information System |
E243144
|
entity |
| Predicate | interoperableWith |
P14647
|
FINISHED |
| Object |
EES-VIS shared biometric matching service
The EES-VIS shared biometric matching service is a European Union system that enables shared biometric identification and verification across the Entry/Exit System (EES) and the Visa Information System (VIS) to support border management and security.
|
E858305
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: EES-VIS shared biometric matching service | Statement: [Visa Information System, interoperableWith, EES-VIS shared biometric matching service]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EES-VIS shared biometric matching service Context triple: [Visa Information System, interoperableWith, EES-VIS shared biometric matching service]
-
A.
Automated Biometric Identification System
The Automated Biometric Identification System is a large-scale U.S. government database and matching system that stores and analyzes biometric data, such as fingerprints and facial images, to verify and identify individuals for security and immigration purposes.
-
B.
Integrated Automated Fingerprint Identification System
The Integrated Automated Fingerprint Identification System is the FBI’s large-scale computerized system for storing, searching, and matching fingerprint and biometric data to support criminal identification and investigative work.
-
C.
Latent and Palm Print Services
Latent and Palm Print Services is a component of the FBI’s Next Generation Identification system that manages, stores, and analyzes latent and palm print data to support biometric identification and criminal investigations.
-
D.
Real-Time Automated Personnel Identification System
The Real-Time Automated Personnel Identification System (RAPIDS) is a U.S. Department of Defense system used to verify identity and issue Common Access Cards and other identification credentials to eligible personnel and dependents.
-
E.
Modified National Institute of Standards and Technology database
The Modified National Institute of Standards and Technology database is a large, standardized collection of handwritten digit images widely used for training and evaluating image processing and machine learning algorithms.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: EES-VIS shared biometric matching service Triple: [Visa Information System, interoperableWith, EES-VIS shared biometric matching service]
Generated description
The EES-VIS shared biometric matching service is a European Union system that enables shared biometric identification and verification across the Entry/Exit System (EES) and the Visa Information System (VIS) to support border management and security.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: EES-VIS shared biometric matching service Target entity description: The EES-VIS shared biometric matching service is a European Union system that enables shared biometric identification and verification across the Entry/Exit System (EES) and the Visa Information System (VIS) to support border management and security.
-
A.
Automated Biometric Identification System
The Automated Biometric Identification System is a large-scale U.S. government database and matching system that stores and analyzes biometric data, such as fingerprints and facial images, to verify and identify individuals for security and immigration purposes.
-
B.
Integrated Automated Fingerprint Identification System
The Integrated Automated Fingerprint Identification System is the FBI’s large-scale computerized system for storing, searching, and matching fingerprint and biometric data to support criminal identification and investigative work.
-
C.
Latent and Palm Print Services
Latent and Palm Print Services is a component of the FBI’s Next Generation Identification system that manages, stores, and analyzes latent and palm print data to support biometric identification and criminal investigations.
-
D.
Real-Time Automated Personnel Identification System
The Real-Time Automated Personnel Identification System (RAPIDS) is a U.S. Department of Defense system used to verify identity and issue Common Access Cards and other identification credentials to eligible personnel and dependents.
-
E.
Modified National Institute of Standards and Technology database
The Modified National Institute of Standards and Technology database is a large, standardized collection of handwritten digit images widely used for training and evaluating image processing and machine learning algorithms.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e0a645348190af91450360a9abed |
completed | April 7, 2026, 10:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d75070c3ac8190b0d50a93d48c9bd9 |
completed | April 9, 2026, 7:08 a.m. |
| NEDg | Description generation | batch_69d7618c9abc819080c4d6669dfb8320 |
completed | April 9, 2026, 8:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d7702ae24481908b0f5319413e81d4 |
completed | April 9, 2026, 9:23 a.m. |
Created at: April 6, 2026, 11:55 a.m.