Triple
T13252253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Women's Soccer League season 2013 |
E315561
|
entity |
| Predicate | goldenGloveCleanSheets |
P25060
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [National Women's Soccer League season 2013, goldenGloveCleanSheets, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goldenGloveCleanSheets Context triple: [National Women's Soccer League season 2013, goldenGloveCleanSheets, 10]
-
A.
mostCleanSheetsClub
Indicates that the club holds the record for the highest number of clean sheets (matches without conceding a goal) in a given context.
-
B.
GoldGloveCount
chosen
Indicates the number of Gold Glove awards an entity has received.
-
C.
GoldenGloveWinner
Indicates that the subject has been awarded the Golden Glove (or Gold Glove) honor, recognizing superior defensive performance in their sport.
-
D.
bestGoalkeeper
Indicates that the subject is considered the top-performing or most skilled goalkeeper among a specified group or context.
-
E.
losingGoaltender
Indicates that a particular goaltender is the one who was in net for the team that lost the game.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f60911081909fa346a054f76c9f |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:24 p.m.