Triple
T33425288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | selección femenina de fútbol de Ecuador |
E855958
|
entity |
| Predicate | númeroParticipacionesMundialFemenino |
P159737
|
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: [selección femenina de fútbol de Ecuador, númeroParticipacionesMundialFemenino, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: númeroParticipacionesMundialFemenino Context triple: [selección femenina de fútbol de Ecuador, númeroParticipacionesMundialFemenino, 1]
-
A.
FIFAWomensWorldCupAppearances
chosen
Indicates the number of times an entity has participated in the FIFA Women's World Cup tournament.
-
B.
worldCupAppearancesCount
Indicates the number of times an entity has participated in the FIFA World Cup tournament.
-
C.
worldCupAppearances
Indicates the number of times an entity has participated in a FIFA World Cup tournament.
-
D.
ConfederationsCupAppearances
Indicates the number of times an entity has participated in the FIFA Confederations Cup tournament.
-
E.
FIFAWomen'sWorldCupAllTimeTopScorer
Indicates that the subject holds the record for scoring the most goals in the history of the FIFA Women's World Cup.
- 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_69f3496fdf0081908c1aa30870ce518b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e47f37848190aadb137c81760f1f |
completed | May 3, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69f6e3da41948190a4cfe866ce184f73 |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:36 a.m.