Triple
T8573260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shahriar County |
E202980
|
entity |
| Predicate | hasAdministrativeCenter |
P1474
|
FINISHED |
| Object | Shahriar |
E742531
|
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: Shahriar | Statement: [Shahriar County, hasAdministrativeCenter, Shahriar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shahriar Context triple: [Shahriar County, hasAdministrativeCenter, Shahriar]
-
A.
Shahriar
chosen
Shahriar is a city in Tehran Province, Iran, known as an urban center within the metropolitan area southwest of Tehran.
-
B.
Shakil
Shakil is a family name notably associated with the character Omar Khayyam Shakil from Salman Rushdie’s novel "Shame."
-
C.
Jasimuddin
Jasimuddin was a renowned Bengali poet and folklorist celebrated for his vivid depictions of rural Bengal and its people.
-
D.
Mazhar
Mazhar is a masculine given name of Arabic origin commonly used in Turkey and several other Muslim-majority countries.
-
E.
Khaliquzzaman
Khaliquzzaman was a prominent South Asian Muslim politician and leader active during the Indian independence movement and the early years of Pakistan.
- 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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea458c1081908e79bee2cbf97207 |
completed | March 31, 2026, 3:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea88464b88190983e22e70bf38e63 |
completed | April 2, 2026, 5:33 p.m. |
Created at: March 30, 2026, 6:21 p.m.