Triple
T26646048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1970 American League Championship Series |
E668909
|
entity |
| Predicate | runnerUpConsecutiveALCSTitleLosses |
P11630
|
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: [1970 American League Championship Series, runnerUpConsecutiveALCSTitleLosses, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runnerUpConsecutiveALCSTitleLosses Context triple: [1970 American League Championship Series, runnerUpConsecutiveALCSTitleLosses, 2]
-
A.
seasonRecordLosses
Indicates the number of games a team lost during a specific season.
-
B.
runnerUpConsecutiveAppearances
chosen
Indicates that an entity has achieved runner-up status in a competition for a specified number of consecutive appearances or editions.
-
C.
mostGamesLostBy
Indicates that one entity holds the record for having lost the greatest number of games to another entity.
-
D.
regularSeasonLosses
Indicates the number of games a team lost during the regular season portion of a competition or league.
-
E.
mostConsecutiveWinsCount
Indicates the highest number of wins achieved in a row within a given sequence or context.
- 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_69ee9d00eb5481908d6c6d0ada2f0c9a |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f61fd623bc819091df736cf3419b99 |
completed | May 2, 2026, 4:01 p.m. |
| PD | Predicate disambiguation | batch_69f61b3d23f481908dfec27adace900a |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 2:31 a.m.