Triple
T15472409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leslie Bibb |
E376694
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Zookeeper |
E773395
|
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: Zookeeper | Statement: [Leslie Bibb, notableWork, Zookeeper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zookeeper Context triple: [Leslie Bibb, notableWork, Zookeeper]
-
A.
Zookeeper
chosen
Zookeeper is a 2011 comedy film starring Kevin James as a kindhearted animal caretaker who receives romantic advice from talking zoo animals.
-
B.
Zoot
Zoot is a fictional animal character name commonly used in entertainment and media, often evoking a quirky or playful creature.
-
C.
The Zookeeper
The Zookeeper is a film featuring Czech actor Karel Roden in a prominent role.
-
D.
Plottier
Plottier is a city in the Neuquén Province of Argentina, located in the Patagonian region and known for its agricultural production and proximity to the provincial capital, Neuquén.
-
E.
Meerkat
Meerkat is a live video streaming app that briefly gained prominence for enabling users to broadcast real-time video from their smartphones to social media audiences.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6c57308190b4cfe661c26addd4 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2d075e64819097061ef4c205577e |
completed | May 9, 2026, 12:48 p.m. |
Created at: April 10, 2026, 3:33 a.m.