Triple
T19993256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York's Boldest |
E494113
|
entity |
| Predicate | refersToProfession |
P138259
|
FINISHED |
| Object | correction officer |
—
|
LITERAL 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: correction officer | Statement: [New York's Boldest, refersToProfession, correction officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToProfession Context triple: [New York's Boldest, refersToProfession, correction officer]
-
A.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
B.
recognizesProfession
Indicates that one entity acknowledges or identifies another entity’s professional role or occupation as such.
-
C.
relatedProfession
Indicates that two entities have professions that are connected or associated in some meaningful way, such as being in the same field, industry, or professional domain.
-
D.
representsProfessionIn
Indicates that an entity holds or is associated with a particular profession within a specified context, domain, or location.
-
E.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
- F. None of above. chosen
Provenance (4 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fe2036c8190b9f313215ad44e87 |
completed | April 20, 2026, 5:18 p.m. |
| PD | Predicate disambiguation | batch_69e537fd311881908448f2aea8b4812e |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c42c688190a22f4d31ec692377 |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 11, 2026, 3:31 p.m.