Triple
T11175828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marta |
E264408
|
entity |
| Predicate | TheBestFIFAWomen'sPlayerAward |
P40879
|
FINISHED |
| Object | 2018 |
—
|
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: 2018 | Statement: [Marta, TheBestFIFAWomen'sPlayerAward, 2018]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: TheBestFIFAWomen'sPlayerAward Context triple: [Marta, TheBestFIFAWomen'sPlayerAward, 2018]
-
A.
FIFA100Women
Indicates that a person is recognized as one of the top 100 women footballers selected by FIFA.
-
B.
FIFABallonDorWinner
Indicates that the subject is the football player who won the FIFA Ballon d'Or award in the specified year or context.
-
C.
fifaWorldPlayerOfTheYear
chosen
Indicates that one entity has been awarded the FIFA World Player of the Year title for a given year or period.
-
D.
FWAPlayerOfTheYear
Indicates that the subject has been selected as the Football Writers' Association Player of the Year.
-
E.
topGoalScorerAward
Indicates that an entity receives an award for scoring the highest number of goals in a given competition or season.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8987e1081909b28a0bdb866beae |
completed | April 9, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69d75cf0e6e88190973694abe2990973 |
completed | April 9, 2026, 8:01 a.m. |
Created at: April 8, 2026, 9:29 p.m.