Triple
T25375153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liz Cambage |
E633036
|
entity |
| Predicate | singleGamePointsRecordTeam |
P158249
|
FINISHED |
| Object | Dallas Wings |
—
|
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: Dallas Wings | Statement: [Liz Cambage, singleGamePointsRecordTeam, Dallas Wings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: singleGamePointsRecordTeam Context triple: [Liz Cambage, singleGamePointsRecordTeam, Dallas Wings]
-
A.
singleGamePointsRecord
chosen
Indicates that an entity holds the record for the highest number of points scored in a single game.
-
B.
recordTeam
Indicates that a particular team is officially documented or stored as part of a record in a system or dataset.
-
C.
singleGamePointsRecordYear
Indicates the year in which a single-game points record was set.
-
D.
collegeTeamPointsRecord
Indicates the record of points scored by a college sports team, typically summarizing their scoring performance over a season or set of games.
-
E.
singleGameTouchdownRecordTeam
Indicates the team that holds the record for the most touchdowns scored in a single game.
- 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_69e75a90c0dc819092f928b6ea0ecc72 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f55e591ffc8190aac5b03db15e45da |
completed | May 2, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69f4683b34748190818428489a226124 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 1:38 p.m.