Triple
T19085125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto Toros |
E467127
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Toros |
—
|
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: Toros | Statement: [Toronto Toros, shortName, Toros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toros Context triple: [Toronto Toros, shortName, Toros]
-
A.
Toros
Toros is the nickname of Major League Soccer club FC Dallas, reflecting the team's bull-themed identity.
-
B.
Toros
The Toros are the athletic teams representing California State University, Dominguez Hills in intercollegiate sports.
-
C.
Toros
Toros is the abbreviated name of the Austin Toros, a professional basketball team that competed in the NBA Development League.
-
D.
Toros
chosen
Toros is the short name of the Toronto Toros, a former World Hockey Association team based in Toronto, Canada.
-
E.
Toroslar
Toroslar is a district and municipality in southern Turkey known for its mountainous terrain and proximity to the city of Mersin.
- 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_69d8dd04f4488190b1121cc53ef2bfd6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e346f57c8190a6299e09a0be9e05 |
completed | April 20, 2026, 8:26 a.m. |
Created at: April 10, 2026, 12:04 p.m.