Triple
T659168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Major League Soccer regular season |
E11715
|
entity |
| Predicate | drawPoints |
P8178
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Major League Soccer regular season, drawPoints, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drawPoints Context triple: [Major League Soccer regular season, drawPoints, 1]
-
A.
draws
Indicates that one entity creates a visual representation or image of another entity.
-
B.
plotElement
Indicates that one entity functions as a narrative component or structural element within the storyline of another entity (such as a work of fiction or media).
-
C.
hasNumberOfPoints
Indicates that an entity is associated with a specific count of points it possesses or comprises.
-
D.
drawsLesson
Indicates that one entity derives or infers a lesson or conclusion from another entity or situation.
-
E.
matchPointsDraw
chosen
Indicates that the entities receive or share an equal number of points as a result of a drawn or tied 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_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.