Triple
T2771579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2007 National League pennant |
E61467
|
entity |
| Predicate | gamesWonByRunnerUp |
P43073
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [2007 National League pennant, gamesWonByRunnerUp, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gamesWonByRunnerUp Context triple: [2007 National League pennant, gamesWonByRunnerUp, 0]
-
A.
hasNationalRunnerUpFinishes
Indicates that an entity has achieved one or more second-place (runner-up) finishes in a national-level competition or championship.
-
B.
runnerUp
Indicates that one entity finished in second place relative to another in a competition or ranking.
-
C.
runnerUpScore
Indicates the score achieved by the participant or entity that finished in second place in a competition or ranking.
-
D.
secondRoundWinner
Indicates that the subject is the winner of the second round in a multi-round competition, contest, or process.
-
E.
runnerUpCountry
Indicates the country that finished in second place in a competition or ranking.
- 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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdcfed608819080988e93df7bdf7c |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abddcc348081908b5f760899389d4f |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 9:57 p.m.