Triple
T659169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Major League Soccer regular season |
E11715
|
entity |
| Predicate | lossPoints |
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: [Major League Soccer regular season, lossPoints, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lossPoints Context triple: [Major League Soccer regular season, lossPoints, 0]
-
A.
loserPoints
chosen
Indicates the number of points awarded to or accumulated by the losing side in a competitive event or comparison.
-
B.
losses
Indicates that an entity experiences a decrease in value, quantity, or advantage as a result of some event or comparison.
-
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.
lostPowerIn
Indicates that an entity has experienced a loss of electrical or functional power while in or at a specified location or context.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0f55f7481909e052a25bd12d455 |
completed | March 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69a49d1406ec8190abf546549264c85d |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.