Triple
T16745742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UTEP Miners athletic program |
E406945
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object | Paydirt Pete |
E403343
|
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: Paydirt Pete | Statement: [UTEP Miners athletic program, mascot, Paydirt Pete]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paydirt Pete Context triple: [UTEP Miners athletic program, mascot, Paydirt Pete]
-
A.
Paydirt Pete
chosen
Paydirt Pete is the pickaxe-wielding prospector mascot of the University of Texas at El Paso, symbolizing the school’s mining heritage and athletic spirit.
-
B.
Paymer
Paymer is a surname most notably associated with American character actor David Paymer, known for his extensive work in film and television.
-
C.
Payless Foods
Payless Foods is a local grocery store serving the community of Freeland on Whidbey Island in Washington State.
-
D.
Papa Johns
Papa Johns is a major American pizza restaurant franchise known for its delivery and carryout services worldwide.
-
E.
Papa Murphy's
Papa Murphy's is a take-and-bake pizza chain where customers purchase uncooked pizzas to bake at home.
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3aa223aa88190a3c1805ece7317e2 |
completed | April 18, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a52033748190ae207d72d437236b |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 5:21 a.m.