Triple
T38435882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2006 WNBA Finals |
E903939
|
entity |
| Predicate | championshipTitleNumberForFranchise |
P32585
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [2006 WNBA Finals, championshipTitleNumberForFranchise, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: championshipTitleNumberForFranchise Context triple: [2006 WNBA Finals, championshipTitleNumberForFranchise, 2]
-
A.
championshipNumberForFranchise
chosen
Indicates the number of championships that have been won by a given franchise.
-
B.
championTitleCountFranchise
Indicates the number of championship titles a franchise has won.
-
C.
championshipNumberForTeam
Indicates the total number of championships that a given team has won.
-
D.
consecutiveTitleNumberForChampion
Indicates that the associated number represents how many titles a champion has won consecutively up to that point.
-
E.
totalFranchiseTitleNumber
Indicates the total count of titles associated with a given franchise across all its installments or entries.
- 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_69f76e6a2024819081aa04f4932f89d2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd2a215d6c8190a1a428ccaee603f1 |
completed | May 8, 2026, 12:11 a.m. |
| PD | Predicate disambiguation | batch_69fd28ef19688190bb8370f2812a43e7 |
completed | May 8, 2026, 12:06 a.m. |
Created at: May 3, 2026, 4:31 p.m.