Triple
T6048952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qatar Campus |
E134738
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Doha |
E12693
|
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: Doha | Statement: [Qatar Campus, city, Doha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doha Context triple: [Qatar Campus, city, Doha]
-
A.
Doha
chosen
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.
-
B.
Al-Doha
Al-Doha is a Palestinian town located in the Bethlehem Governorate of the West Bank.
-
C.
Al Rayyan
Al Rayyan is a major Qatari city known for its rapid urban development, sports facilities, and proximity to the capital, Doha.
-
D.
Abu Dhabi
Abu Dhabi is the capital and second-most populous city of the United Arab Emirates, known for its vast oil wealth, modern skyline, and role as a major political and economic center in the Arab world.
-
E.
Kuwait City
Kuwait City is the capital and largest city of Kuwait, serving as a major political, economic, and cultural center on the Persian Gulf.
- 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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056f387cc8190920b846995761aec |
completed | March 22, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c113a65164819090883dbad3be5026 |
completed | March 23, 2026, 10:19 a.m. |
Created at: March 22, 2026, 4:09 p.m.