Triple
T13381925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Education City Stadium |
E319338
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Al Rayyan |
E81825
|
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 Rayyan | Statement: [Education City Stadium, city, Al Rayyan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Al Rayyan Context triple: [Education City Stadium, city, Al Rayyan]
-
A.
Al Rayyan
chosen
Al Rayyan is a major Qatari city known for its rapid urban development, sports facilities, and proximity to the capital, Doha.
-
B.
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.
-
C.
Al-Doha
Al-Doha is a Palestinian town located in the Bethlehem Governorate of the West Bank.
-
D.
Al Wakrah
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.
-
E.
Doha
Doha is the rapidly developing capital and largest city of Qatar, known for its modern skyline, cultural institutions, and role as a major political and economic center in the Arab world.
- 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadce694788190881d1feac5b75720 |
completed | April 11, 2026, 11:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7268b09808190b4ffd1db72e1f1f1 |
completed | May 3, 2026, 10:42 a.m. |
Created at: April 9, 2026, 9:33 p.m.