Triple
T21260922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zana Khan District |
E523996
|
entity |
| Predicate | administrativeCenter |
P1474
|
FINISHED |
| Object | Zana Khan |
—
|
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: Zana Khan | Statement: [Zana Khan District, administrativeCenter, Zana Khan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zana Khan Context triple: [Zana Khan District, administrativeCenter, Zana Khan]
-
A.
Zana Khan
chosen
Zana Khan is a town in Ghazni Province, Afghanistan, serving as the administrative center of Zana Khan District.
-
B.
Zara Kaleel
Zara Kaleel is the central character in Kia Abdullah’s legal thriller series, a British-Muslim barrister known for her fierce pursuit of justice in complex, emotionally charged court cases.
-
C.
Nilofer Khan
Nilofer Khan is an Indian academic and administrator who became the first woman to serve as Vice-Chancellor of the University of Kashmir.
-
D.
Ayesha Takia
Ayesha Takia is an Indian actress best known for her work in Hindi films during the 2000s, including popular roles in movies like "Dor" and "Wanted."
-
E.
Sarai Kale Khan
Sarai Kale Khan is a locality and major bus terminal area in Delhi, India, known as a key intercity transport hub.
- 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_69e0b5156d7881909bd4f83676590715 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e735e6a0448190ad412a8fcbbd8ff0 |
completed | April 21, 2026, 8:31 a.m. |
Created at: April 16, 2026, 3:59 p.m.