Triple
T1775466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Bowl XVIII |
E38966
|
entity |
| Predicate | losingPoints |
P7115
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Super Bowl XVIII, losingPoints, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: losingPoints Context triple: [Super Bowl XVIII, losingPoints, 9]
-
A.
loserPoints
chosen
Indicates the number of points awarded to or accumulated by the losing side in a competitive event or comparison.
-
B.
losingState
Indicates that a particular game or competitive situation is in a condition where the player or side in question is currently losing or destined to lose under optimal play.
-
C.
loserScore
Indicates the number of points or score achieved by the losing participant in a competitive event or comparison.
-
D.
points
Indicates that one entity directs attention, focus, or a physical/abstract indication toward another entity or location.
-
E.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
- 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_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab17e368048190b7b73d156400f772 |
completed | March 6, 2026, 6:07 p.m. |
| PD | Predicate disambiguation | batch_69aa61cd4c1c8190a8dff391f5642bfe |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.