Triple
T509501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lou Gehrig |
E10573
|
entity |
| Predicate | consecutiveGamesPlayed |
P14549
|
FINISHED |
| Object | 2130 |
—
|
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: 2130 | Statement: [Lou Gehrig, consecutiveGamesPlayed, 2130]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: consecutiveGamesPlayed Context triple: [Lou Gehrig, consecutiveGamesPlayed, 2130]
-
A.
consecutiveWinsInSeason
Indicates that one entity achieved a specified number of back-to-back victories within a single season.
-
B.
gamesPlayed
Indicates the number or set of games that an entity has participated in or completed.
-
C.
runnerUpConsecutiveAppearances
Indicates that an entity has achieved runner-up status in a competition for a specified number of consecutive appearances or editions.
-
D.
seriesWinningGame
Indicates that a particular game is the decisive or clinching game in which one side wins the overall series.
-
E.
previousGames
Indicates that one entity represents games that occurred earlier in time relative to another entity.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f164a9d48190b525a97b5c06ffe2 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edfe236481909901cc7d4281b33c |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eebbd70481908b462296671de67b |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.