Triple
T24380649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cynthia Cooper |
E614599
|
entity |
| Predicate | teamChampionshipsWon |
P155992
|
FINISHED |
| Object | 4 WNBA championships with Houston Comets |
—
|
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: 4 WNBA championships with Houston Comets | Statement: [Cynthia Cooper, teamChampionshipsWon, 4 WNBA championships with Houston Comets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teamChampionshipsWon Context triple: [Cynthia Cooper, teamChampionshipsWon, 4 WNBA championships with Houston Comets]
-
A.
team2ChampionshipHistory
Indicates the record of championships that the second team has participated in or won over time.
-
B.
teamChampionshipName
Indicates the name or title of the championship associated with a particular team.
-
C.
hasTeamsChampionship
Indicates that an entity has won at least one championship title in a teams-based competition or league.
-
D.
teamChampionshipsUnderOwnership
Indicates the number of championships a team has won during the period it has been under a specific ownership.
-
E.
teamChampionshipStreak
Indicates the number of consecutive championships a team has won over a continuous sequence of seasons or tournaments.
- 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_69e2d7e362e481909e32fe4ef8269d4f |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f293db31c481908484ab28921cd5fd |
completed | April 29, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69f287c4a2b48190b80fb7a3c0e9b018 |
completed | April 29, 2026, 10:35 p.m. |
| PDg | Predicate description generation | batch_69f28f4d978c81908310c01def2514cc |
completed | April 29, 2026, 11:07 p.m. |
Created at: April 18, 2026, 2:03 a.m.