Triple
T2342838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English First Division 1964–65 |
E45063
|
entity |
| Predicate | usesPointsForLoss |
P38857
|
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: [English First Division 1964–65, usesPointsForLoss, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesPointsForLoss Context triple: [English First Division 1964–65, usesPointsForLoss, 0]
-
A.
loserPoints
Indicates the number of points awarded to or accumulated by the losing side in a competitive event or comparison.
-
B.
usesLossFunction
Indicates that one entity employs a particular loss function as part of its optimization or learning process.
-
C.
loserScore
Indicates the number of points or score achieved by the losing participant in a competitive event or comparison.
-
D.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
-
E.
earnsMorePointsThan
Indicates that one entity receives a greater number of points than another entity in a given context or comparison.
- 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_69a88917935081909b755dbf38e81024 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abcade3c808190ab3803538ccbe620 |
completed | March 7, 2026, 6:51 a.m. |
| PD | Predicate disambiguation | batch_69abc59616a8819099711834e6f1ccd6 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abcadd2a0c8190b6973d390e98bd66 |
completed | March 7, 2026, 6:51 a.m. |
Created at: March 4, 2026, 7:52 p.m.