Triple
T8828548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eric S. Raymond |
E210076
|
entity |
| Predicate | alternateName |
P39
|
FINISHED |
| Object | ESR |
E210076
|
NE FINISHED |
How this triple was built (2 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: ESR | Statement: [Eric S. Raymond, alternateName, ESR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ESR Context triple: [Eric S. Raymond, alternateName, ESR]
-
A.
ESR
ESR is a major professional organization representing radiologists and promoting the field of radiology across Europe.
-
B.
ESR
chosen
ESR is the commonly used abbreviation for Eric S. Raymond, an influential American software developer and open-source advocate.
-
C.
ESRA
ESRA is a U.S. federal law that overhauled education research and statistics, creating the Institute of Education Sciences to improve the quality and use of evidence in education policy and practice.
-
D.
ESGR
ESGR is a U.S. Department of Defense program that promotes cooperation and understanding between Reserve Component service members and their civilian employers.
-
E.
ESE
ESE is a highly competitive Indian national-level examination conducted by the Union Public Service Commission to recruit engineers for prestigious technical and managerial positions in various government departments and public sector organizations.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca8365b28081909e48e45e95dfc405 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc604c52a48190807e46c15e3c1558 |
completed | April 1, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf896382708190a08c6bacf1157066 |
completed | April 3, 2026, 9:33 a.m. |
Created at: March 30, 2026, 6:47 p.m.