Triple
T16007609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Tilton |
E388257
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Zoo |
E379597
|
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: Zoo | Statement: [Chris Tilton, notableWork, Zoo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zoo Context triple: [Chris Tilton, notableWork, Zoo]
-
A.
Zoo
chosen
"Zoo" is a television drama series based on James Patterson’s novel, depicting a global uprising of animals against humanity.
-
B.
Zoo
Zoo is a skateboarding level set in an animal park environment featured in the video game Tony Hawk's Pro Skater 4.
-
C.
Zoo
Zoo is the nickname of Kenneth Petty, an American music industry figure best known as the husband of rapper Nicki Minaj and for his criminal record.
-
D.
The Zoo
"The Zoo" is the raucous student section and fan base known for creating an intense home-field atmosphere at Arizona Stadium, home of the University of Arizona Wildcats football team.
-
E.
The Zoo
The Zoo is the energetic student cheering section for the University of Montana Grizzlies football team, known for its loud, passionate game-day atmosphere.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15800e3608190bd3e1123ccc6c326 |
completed | April 16, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf22db3481909141ddef151d0341 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:55 a.m.