Triple
T11459275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carolina Ruiz Castillo |
E271609
|
entity |
| Predicate | WorldCupWin |
P99677
|
FINISHED |
| Object | downhill in Méribel 2013 |
—
|
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: downhill in Méribel 2013 | Statement: [Carolina Ruiz Castillo, WorldCupWin, downhill in Méribel 2013]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: WorldCupWin Context triple: [Carolina Ruiz Castillo, WorldCupWin, downhill in Méribel 2013]
-
A.
WorldCupWins
Indicates the number of times an entity (typically a national team) has won the FIFA World Cup tournament.
-
B.
WorldCupVictories
Indicates the number of times an entity has won the FIFA World Cup tournament.
-
C.
worldCupWinnerWith
Indicates that one entity is the winner of a specified FIFA World Cup tournament associated with the other entity.
-
D.
numberOfWorldCupWins
Indicates how many times an entity has won the FIFA World Cup tournament.
-
E.
WorldCupRunnerUp
Indicates that an entity finished in second place in a FIFA World Cup tournament, losing in the final match.
- F. None of above. chosen
Provenance (4 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f2138081909408c7916cef99c9 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:35 p.m.