Triple
T419923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Quinn |
E8077
|
entity |
| Predicate | startTimeAsDefensiveCoordinatorForDallasCowboys |
P14710
|
FINISHED |
| Object | 2021 |
—
|
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: 2021 | Statement: [Dan Quinn, startTimeAsDefensiveCoordinatorForDallasCowboys, 2021]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startTimeAsDefensiveCoordinatorForDallasCowboys Context triple: [Dan Quinn, startTimeAsDefensiveCoordinatorForDallasCowboys, 2021]
-
A.
headCoachStartYear
Indicates the year in which an individual began serving as the head coach of a team or organization.
-
B.
hiredAsHeadCoachYear
Indicates the year in which an individual was hired to serve as the head coach of a team or organization.
-
C.
wonSuperBowlAsHeadCoachWith
Indicates that one entity served as the head coach of a team that won the Super Bowl with the other entity (the team) during that championship season.
-
D.
numberOfSuperBowlsWonAsHeadCoach
Indicates the total count of Super Bowl championships an individual has won while serving in the role of head coach.
-
E.
numberOfSuperBowlsWonAsAssistantCoach
Indicates the total count of Super Bowls that an individual has won specifically while serving in the role of an assistant coach.
- F. None of above. chosen
Provenance (4 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edd3b948819097d96c73d0a0f699 |
completed | Feb. 28, 2026, 1:29 p.m. |
| PDg | Predicate description generation | batch_69a2eeb8545c8190a2b8517e7ed5b92e |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.