Triple
T2594117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamika Catchings |
E58188
|
entity |
| Predicate | WNBAretirementSeason |
P40081
|
FINISHED |
| Object | 2016 |
—
|
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: 2016 | Statement: [Tamika Catchings, WNBAretirementSeason, 2016]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: WNBAretirementSeason Context triple: [Tamika Catchings, WNBAretirementSeason, 2016]
-
A.
WNBAFinalsAppearances
Indicates the number of times an entity has participated in the WNBA Finals series.
-
B.
reachedWNBAFinals
Indicates that an entity (typically a team) advanced to and competed in the championship series of the WNBA season.
-
C.
numberOfWNBAChampionships
Indicates the count of WNBA championship titles that an entity has won.
-
D.
tookHiatusFromWNBAInYear
Indicates that a person temporarily stopped playing or participating in the WNBA during a specified year.
-
E.
WNBAAllWNBAFirstTeamSelection
Indicates that an individual has been chosen for the WNBA All-WNBA First Team in a given season, recognizing them as one of the league’s top players at their position.
- 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_69ab4ac019c8819094add11c46706e32 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd427f58c8190af1c1a9724158c96 |
completed | March 7, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69abd0d344988190a18dd93b13e002e6 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd2baee308190bdaa41ef1f6bc9cc |
completed | March 7, 2026, 7:24 a.m. |
Created at: March 6, 2026, 9:49 p.m.