Triple
T403908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malta |
E9342
|
entity |
| Predicate | ISO3166-1Alpha3 |
P189
|
FINISHED |
| Object |
MLT
MLT is the three-letter ISO 3166-1 alpha-3 country code assigned to Malta.
|
E51447
|
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: MLT | Statement: [Malta, ISO3166-1Alpha3, MLT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MLT Context triple: [Malta, ISO3166-1Alpha3, MLT]
-
A.
SLT
SLT is a well-equipped, mid-to-upper trim level commonly associated with GMC trucks and SUVs, offering upgraded comfort, technology, and appearance features.
-
B.
MTO
MTO is an abbreviation for the Mediterranean Theater of Operations, the World War II combat zone encompassing Allied and Axis military campaigns around the Mediterranean Sea.
-
C.
DTL
DTL is a type of linear accelerator structure that uses a series of drift tubes within an RF cavity to efficiently accelerate charged particle beams.
-
D.
METS
METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
-
E.
NMTI
NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
- 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: MLT Triple: [Malta, ISO3166-1Alpha3, MLT]
Generated description
MLT is the three-letter ISO 3166-1 alpha-3 country code assigned to Malta.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MLT Target entity description: MLT is the three-letter ISO 3166-1 alpha-3 country code assigned to Malta.
-
A.
SLT
SLT is a well-equipped, mid-to-upper trim level commonly associated with GMC trucks and SUVs, offering upgraded comfort, technology, and appearance features.
-
B.
MTO
MTO is an abbreviation for the Mediterranean Theater of Operations, the World War II combat zone encompassing Allied and Axis military campaigns around the Mediterranean Sea.
-
C.
DTL
DTL is a type of linear accelerator structure that uses a series of drift tubes within an RF cavity to efficiently accelerate charged particle beams.
-
D.
METS
METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
-
E.
NMTI
NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eca226fc81909d6ccc38a637daa6 |
completed | Feb. 28, 2026, 1:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a413f43f1481908b095300fd6630c9 |
completed | March 1, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69a414c6635c819090c5c1aedc353d65 |
completed | March 1, 2026, 10:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a415ba975881908034177cf892ca3e |
completed | March 1, 2026, 10:32 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.