Triple
T7827908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AFC Futsal Asian Cup |
E181291
|
entity |
| Predicate | mostTitlesTeamTitles |
P79236
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [AFC Futsal Asian Cup, mostTitlesTeamTitles, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mostTitlesTeamTitles Context triple: [AFC Futsal Asian Cup, mostTitlesTeamTitles, 12]
-
A.
team1LeagueTitles
Indicates the number of league titles that the first team has won.
-
B.
numberOfLeagueTitles
Indicates the total count of league championship titles that an entity has won.
-
C.
team2LeagueTitlesContext
Indicates that the second team has won league titles within a specified contextual scope (such as a particular time period, competition, or condition).
-
D.
mostDivisionTitlesTeam
Indicates that the subject team holds the record for having won the greatest number of division titles compared to all other teams.
-
E.
team2LeagueTitles
Indicates that a given team has won a specified number of league titles.
- 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_69ca8282ccec819083c48efb72d21cf9 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb04a97d748190b1924890b1328bf1 |
completed | March 30, 2026, 11:18 p.m. |
| PD | Predicate disambiguation | batch_69cae91ae008819098e56bbe51143b31 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7855a3c81908b9318f7186fc0c0 |
completed | March 30, 2026, 10:21 p.m. |
Created at: March 30, 2026, 4:43 p.m.