Triple
T36917074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Tech vs Michigan State |
E913071
|
entity |
| Predicate | TexasTechTurnovers |
P41443
|
FINISHED |
| Object | 11 |
—
|
LITERAL 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: 11 | Statement: [Texas Tech vs Michigan State, TexasTechTurnovers, 11]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: TexasTechTurnovers Context triple: [Texas Tech vs Michigan State, TexasTechTurnovers, 11]
-
A.
TexasTechCoach
Indicates that one entity serves as a coach for the Texas Tech athletic program.
-
B.
cowboysTurnovers
Indicates the number of times the Cowboys lose possession of the ball to the opposing team through fumbles or interceptions.
-
C.
TexasTechFieldGoalPercentage
Indicates the percentage of field goal attempts successfully made by Texas Tech in a given context (such as a game or season).
-
D.
coltsTurnovers
Indicates the number of times the Colts lost possession of the ball to the opposing team through turnovers (such as interceptions or fumbles).
-
E.
turnoversTotal
chosen
Indicates the total number of times possession changes from one entity to another, typically due to errors, losses, or rule-based transfers.
- F. None of above.
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_69f76e885b848190bad82c87e9525486 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
Created at: May 3, 2026, 4:13 p.m.