Triple
T11068402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Islam in Yemen |
E261682
|
entity |
| Predicate | importantCity |
P3940
|
FINISHED |
| Object | Saada |
E522204
|
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: Saada | Statement: [Islam in Yemen, importantCity, Saada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saada Context triple: [Islam in Yemen, importantCity, Saada]
-
A.
Saada
chosen
Saada is a city and governorate in northern Yemen known as a stronghold and historical center of the Houthi movement.
-
B.
Salha
Salha is a Jordanian princess and member of the Hashemite royal family.
-
C.
Sauda
Sauda is a small industrial town and municipality in Rogaland county, Norway, known for its hydropower-based industry and dramatic fjord and mountain landscape.
-
D.
Salwa
Salwa is a coastal residential district in Kuwait, located within the Hawalli Governorate and known for its mix of housing, schools, and local amenities.
-
E.
Taybeh
Taybeh is a predominantly Christian Palestinian village in the central West Bank, known for its historic churches and its locally brewed Taybeh beer.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7992164d88190a01ed567b2529227 |
completed | April 9, 2026, 12:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c8b2d6e881909eeddf1e6427ad5c |
completed | April 18, 2026, 6:08 p.m. |
Created at: April 8, 2026, 9:26 p.m.