Triple
T36917065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas Tech vs Michigan State |
E913071
|
entity |
| Predicate | TexasTechCoach |
P187220
|
FINISHED |
| Object | Chris Beard |
—
|
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: Chris Beard | Statement: [Texas Tech vs Michigan State, TexasTechCoach, Chris Beard]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: TexasTechCoach Context triple: [Texas Tech vs Michigan State, TexasTechCoach, Chris Beard]
-
A.
HoustonCoach
Indicates that an individual serves as a coach for a sports team or organization based in Houston.
-
B.
dallasCoach
Indicates that one entity serves as the coach of a Dallas-based sports team associated with the other entity.
-
C.
FortWayneCoach
Indicates that one entity serves as a coach associated with Fort Wayne (such as a team, organization, or institution based there) in relation to another entity.
-
D.
coachSince
Indicates that one entity has been serving as a coach for another entity starting from a specified point in time.
-
E.
playingCoach
Indicates a person who simultaneously serves as both a player and a coach for the same team or organization.
- 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_69f76e885b848190bad82c87e9525486 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
| PDg | Predicate description generation | batch_69fb34e4906c8190abb1c293fb84329a |
completed | May 6, 2026, 12:32 p.m. |
Created at: May 3, 2026, 4:13 p.m.