Triple
T23861486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olympic football Asian qualifiers |
E592460
|
entity |
| Predicate | usesRankingForSeeding |
P12770
|
FINISHED |
| Object | FIFA World Ranking (women) |
—
|
NE NERFINISHED |
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: FIFA World Ranking (women) | Statement: [Olympic football Asian qualifiers, usesRankingForSeeding, FIFA World Ranking (women)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesRankingForSeeding Context triple: [Olympic football Asian qualifiers, usesRankingForSeeding, FIFA World Ranking (women)]
-
A.
usesRank
chosen
Indicates that one entity applies or relies on a ranking or ordered level system associated with another entity.
-
B.
hasRankingFactor
Indicates that one entity contributes as a factor to determining the ranking or ordered position of another entity.
-
C.
hasRankingAlgorithm
Indicates that an entity uses or is associated with a specific algorithm for ranking or ordering items.
-
D.
hasSeparateRanking
Indicates that an entity is evaluated or ordered independently from others, with its own distinct ranking.
-
E.
usesRankStructure
Indicates that an entity organizes its members or components according to a defined hierarchical rank structure.
- 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_69e25d22eb488190914b193aff952e83 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1cae0b4bc819089f491d9e817d160 |
completed | April 29, 2026, 9:09 a.m. |
| PD | Predicate disambiguation | batch_69f1614612b481908c45d99e588882f9 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:13 p.m.