Triple
T4998627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1956 NBA Finals |
E112308
|
entity |
| Predicate | gameCountWonByRunnerUp |
P43073
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [1956 NBA Finals, gameCountWonByRunnerUp, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gameCountWonByRunnerUp Context triple: [1956 NBA Finals, gameCountWonByRunnerUp, 1]
-
A.
gamesWonByRunnerUp
chosen
Indicates the number of games won by the runner-up in a competition or match.
-
B.
runnerUpScore
Indicates the score achieved by the participant or entity that finished in second place in a competition or ranking.
-
C.
stateOfRunnerUpTeam
Indicates the state or region associated with the team that finished as the runner-up in a competition or event.
-
D.
firstRoundRunnerUp
Indicates that an entity finished in second place in the first round of a competition or selection process.
-
E.
hasNationalRunnerUpFinishes
Indicates that an entity has achieved one or more second-place (runner-up) finishes in a national-level competition or championship.
- 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_69bd4432b32c81909f3b3c6bd10f0653 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7472a1dc8190942f568a81fdd961 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd714aee2481908fb0dd5fa2daf3a1 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:34 p.m.