Triple
T14947327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2022 NBA Finals |
E372698
|
entity |
| Predicate | championFranchiseTitleCount |
P32585
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [2022 NBA Finals, championFranchiseTitleCount, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: championFranchiseTitleCount Context triple: [2022 NBA Finals, championFranchiseTitleCount, 7]
-
A.
mostChampionshipTitlesClub
Indicates that a club holds the highest number of championship titles within a given competition or context.
-
B.
consecutiveTitlesForChampion
Indicates that a champion has won multiple titles in succession without interruption.
-
C.
winnerTitleCount
Indicates the number of titles or championships an entity has won.
-
D.
hasMostSuccessfulFranchiseByTitles
Indicates that one entity is the franchise holding the highest number of titles (e.g., championships or awards) within a specified domain compared to all other franchises.
-
E.
championshipNumberForFranchise
chosen
Indicates the number of championships that have been won by a given franchise.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded68e35c481908e47cd68441c5115 |
completed | April 15, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69de9a588c2c8190b1245a1c406f447c |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:39 a.m.