Triple
T10347561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2001 Major League Soccer season |
E243789
|
entity |
| Predicate | pointForLoss |
P7115
|
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: [2001 Major League Soccer season, pointForLoss, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointForLoss Context triple: [2001 Major League Soccer season, pointForLoss, 0]
-
A.
usesPointsForLoss
Indicates that a system or rule assigns or deducts points to represent or account for a loss.
-
B.
loserPoints
chosen
Indicates the number of points awarded to or accumulated by the losing side in a competitive event or comparison.
-
C.
points
Indicates that one entity directs attention, focus, or a physical/abstract indication toward another entity or location.
-
D.
loserScore
Indicates the number of points or score achieved by the losing participant in a competitive event or comparison.
-
E.
pointsForWin
Indicates the number of points awarded to an entity for achieving a win in a given context or competition.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9251654819080d1e3f0ed4ee9d3 |
completed | April 7, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69d4dfa657f481909cc5cc8fec00ad19 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 11:56 a.m.