Triple
T22286287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Memphis Tigers |
E550869
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object | Pouncer |
—
|
NE NERFINISHED |
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: Pouncer | Statement: [Memphis Tigers, mascot, Pouncer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pouncer Context triple: [Memphis Tigers, mascot, Pouncer]
-
A.
Pouncer
chosen
Pouncer is the costumed tiger mascot who represents the University of Memphis Tigers at athletic events and school functions.
-
B.
Pooc
Pooc is a dialect of the Paicî language, an Austronesian language spoken in New Caledonia.
-
C.
Pit Pony
Pit Pony is a Canadian television film and subsequent series about a young boy working with ponies in a coal mine, notable as one of Elliot Page’s early acting roles.
-
D.
Poons
Poons is a surname most notably associated with Larry Poons, an American abstract painter known for his innovative use of color and optical effects.
-
E.
Pippy
Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e44d538819097c6b8f333af3352 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15607d9948190b4b8e9cd7fa4d390 |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 16, 2026, 8:40 p.m.