Triple
T14254394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EBSCOhost |
E353348
|
entity |
| Predicate | includesDatabaseFamily |
P11852
|
FINISHED |
| Object |
ERIC
ERIC is a comprehensive digital library of education-related literature and resources maintained by the U.S. Department of Education.
|
E1089713
|
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: ERIC | Statement: [EBSCOhost, includesDatabaseFamily, ERIC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ERIC Context triple: [EBSCOhost, includesDatabaseFamily, ERIC]
-
A.
ERIC
ERIC is the stock ticker symbol for Ericsson, a major Swedish multinational telecommunications and networking company.
-
B.
ETS
ETS is a nonprofit organization that develops and administers standardized tests and assessments used worldwide for education, certification, and professional licensing.
-
C.
ETS
ETS is the abbreviation for the Educational and Training Services Branch, a unit responsible for developing and delivering education and training programs.
-
D.
ERIC-A
ERIC-A is a class or series of equity securities distinguished by its specific voting rights structure within the ERIC share framework.
-
E.
ERM
ERM is a European Union system designed to reduce exchange rate variability and achieve monetary stability in preparation for economic and monetary union.
- 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: ERIC Triple: [EBSCOhost, includesDatabaseFamily, ERIC]
Generated description
ERIC is a comprehensive digital library of education-related literature and resources maintained by the U.S. Department of Education.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ERIC Target entity description: ERIC is a comprehensive digital library of education-related literature and resources maintained by the U.S. Department of Education.
-
A.
ERIC
ERIC is the stock ticker symbol for Ericsson, a major Swedish multinational telecommunications and networking company.
-
B.
ETS
ETS is a nonprofit organization that develops and administers standardized tests and assessments used worldwide for education, certification, and professional licensing.
-
C.
ETS
ETS is the abbreviation for the Educational and Training Services Branch, a unit responsible for developing and delivering education and training programs.
-
D.
ERIC-A
ERIC-A is a class or series of equity securities distinguished by its specific voting rights structure within the ERIC share framework.
-
E.
ERM
ERM is a European Union system designed to reduce exchange rate variability and achieve monetary stability in preparation for economic and monetary union.
- 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6297f38c819090d7c7fd8bfa2e9e |
completed | April 14, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd325c98288190ba035fb5cc5bcf6b |
completed | May 8, 2026, 12:46 a.m. |
| NEDg | Description generation | batch_69fd33cba18481908f2dfe358017f11b |
completed | May 8, 2026, 12:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd346ffb9c81909ec28e514ea5451b |
completed | May 8, 2026, 12:55 a.m. |
Created at: April 10, 2026, 1:09 a.m.