Triple
T28918556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qatar Clasico |
E733440
|
entity |
| Predicate | featuresClubWithMostLeagueTitlesInQatar |
P27144
|
FINISHED |
| Object | Al Sadd SC |
E199293
|
NE 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: Al Sadd SC | Statement: [Qatar Clasico, featuresClubWithMostLeagueTitlesInQatar, Al Sadd SC]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresClubWithMostLeagueTitlesInQatar Context triple: [Qatar Clasico, featuresClubWithMostLeagueTitlesInQatar, Al Sadd SC]
-
A.
arabCupTitles
Indicates the number of Arab Cup championship titles an entity has won.
-
B.
gulfCupTitles
Indicates the number of Gulf Cup championship titles an entity (typically a national football team) has won.
-
C.
numberOfTitlesOfMostSuccessfulClub
chosen
Indicates the total count of titles won by the club that has achieved the highest number of titles among all clubs in the given context.
-
D.
QatarPosition
Indicates a positional or locational relationship involving Qatar, such as where something is situated in or relative to Qatar.
-
E.
numberOfLeagueTitles
Indicates the total count of league championship titles that an entity has won.
- F. None of above.
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_69f05b0a5cc0819094828367ae204b70 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69fec4cffed08190b5e5e7cc0c87493e |
completed | May 9, 2026, 5:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a26613e946c81909fc0347f8d8e8987 |
completed | June 8, 2026, 6:29 a.m. |
| PD | Predicate disambiguation | batch_69fec2ea7fe08190bd751b39515f69d1 |
completed | May 9, 2026, 5:15 a.m. |
Created at: April 28, 2026, 8:17 a.m.