Triple
T9788274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chef Skinner |
E237541
|
entity |
| Predicate | legalStatusAtStart |
P2250
|
FINISHED |
| Object | acting owner of Gusteau’s restaurant |
—
|
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: acting owner of Gusteau’s restaurant | Statement: [Chef Skinner, legalStatusAtStart, acting owner of Gusteau’s restaurant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalStatusAtStart Context triple: [Chef Skinner, legalStatusAtStart, acting owner of Gusteau’s restaurant]
-
A.
legalStatusAtIssue
Indicates that the legal status of an entity is the central subject of dispute, consideration, or determination in a legal context.
-
B.
legalStatusAtArrival
Indicates the legal status or classification an entity held at the time it first arrived at a particular place or jurisdiction.
-
C.
hasLegalStatus
chosen
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
-
D.
usedLegalStatus
Indicates that one entity applies or relies on the legal status or classification of another entity in a given context.
-
E.
legalStatusBeforeRelease
Indicates the legal condition or classification an entity was subject to prior to its release.
- F. None of above.
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_69ca84da927881909bda80caecad6010 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda2131164819099e8644e40a3cab6 |
completed | April 1, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69cd03d77c6c81909b675955bf113320 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:27 p.m.