Triple
T10418078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giants–Washington rivalry |
E245571
|
entity |
| Predicate | highestScoringGameLosingScore |
P48567
|
FINISHED |
| Object | 41 |
—
|
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: 41 | Statement: [Giants–Washington rivalry, highestScoringGameLosingScore, 41]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: highestScoringGameLosingScore Context triple: [Giants–Washington rivalry, highestScoringGameLosingScore, 41]
-
A.
pointsScoredByLosingTeam
chosen
Indicates the number of points scored by the team that did not win in a given game or match.
-
B.
recordHighScoringForWinner
Indicates that a record is kept of the highest score achieved by the winning entity in a given context or event.
-
C.
loserScore
Indicates the number of points or score achieved by the losing participant in a competitive event or comparison.
-
D.
mostGamesLostBy
Indicates that one entity holds the record for having lost the greatest number of games to another entity.
-
E.
goalsByLosingTeam
Indicates the number of goals scored by the team that ultimately lost the match.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea2938188190a908316d0a0959be |
completed | April 7, 2026, 11:27 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb9d3648190aaabed901f22a8c0 |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:11 p.m.