Triple
T3343448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2022 FIFA World Cup |
E70312
|
entity |
| Predicate | hostCities |
P3207
|
FINISHED |
| Object | Al Wakrah |
E82869
|
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 Wakrah | Statement: [2022 FIFA World Cup, hostCities, Al Wakrah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Al Wakrah Context triple: [2022 FIFA World Cup, hostCities, Al Wakrah]
-
A.
Al Wakrah
chosen
Al Wakrah is a coastal city in southeastern Qatar known for its historic fishing and pearling heritage and as the site of the modern Al Janoub Stadium.
-
B.
Al-Doha
Al-Doha is a Palestinian town located in the Bethlehem Governorate of the West Bank.
-
C.
Al Ain
Al Ain is a historic oasis city in the Emirate of Abu Dhabi in the United Arab Emirates, known for its date palm plantations, forts, and role as a cultural and agricultural center.
-
D.
Al Rayyan
Al Rayyan is a major Qatari city known for its rapid urban development, sports facilities, and proximity to the capital, Doha.
-
E.
Al Khor
Al Khor is a coastal city in northeastern Qatar known for hosting matches at Al Bayt Stadium during the 2022 FIFA World Cup.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ad85a405e48190b6e68de7cf9f319e |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1f06f8c8190a6b7c56ac3f5ff07 |
completed | March 8, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3545cd3508190b3bb81de6feae66e |
completed | March 13, 2026, 12:03 a.m. |
Created at: March 8, 2026, 3:12 p.m.