Triple
T20066676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater Tehran |
E499624
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Pishva |
—
|
NE NERFINISHED |
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: Pishva | Statement: [Greater Tehran, contains, Pishva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pishva Context triple: [Greater Tehran, contains, Pishva]
-
A.
Pishva
chosen
Pishva is a city in Tehran Province, Iran, known as an administrative and local commercial center for the surrounding region.
-
B.
Kangavar
Kangavar is a historic town in western Iran, known for its archaeological remains including a large terraced complex traditionally associated with a temple of Anahita.
-
C.
Sorkheh
Sorkheh is a small city in north-central Iran known for its location within Semnan Province and its semi-arid climate.
-
D.
Bavanat
Bavanat is a small city in southern Iran known for its traditional rural landscapes, gardens, and location within the mountainous region of Fars Province.
-
E.
Sarbisheh
Sarbisheh is a city in eastern Iran that serves as a local administrative and population center within South Khorasan Province.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66379f2cc81908f13a7b216878f12 |
completed | April 20, 2026, 5:33 p.m. |
Created at: April 11, 2026, 3:39 p.m.