Triple
T12606428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nathia Gali |
E300989
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Dunga Gali |
E931831
|
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: Dunga Gali | Statement: [Nathia Gali, near, Dunga Gali]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dunga Gali Context triple: [Nathia Gali, near, Dunga Gali]
-
A.
Bara Gali
chosen
Bara Gali is a small mountain resort town in Pakistan’s Galiyat region, known for its cool climate, forested hills, and scenic hiking opportunities.
-
B.
Dargai
Dargai is a town in Pakistan’s Khyber Pakhtunkhwa province, historically known as a strategic and military site in the Malakand region.
-
C.
Banshiwala
Banshiwala is a Bengali novel by acclaimed writer Shirshendu Mukhopadhyay, known for its evocative storytelling and exploration of human relationships.
-
D.
Bhokar
Bhokar is a legislative assembly constituency in the Nanded district of Maharashtra, India.
-
E.
Lal Darja
Lal Darja is an acclaimed Bengali film by director Buddhadeb Dasgupta, known for its poetic, allegorical exploration of human freedom and social constraints.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954e7f2dc8190a42cab7a0e5ea7f3 |
completed | April 10, 2026, 7:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ecd1b748190bd961497b30e1ae5 |
completed | May 2, 2026, 8:30 p.m. |
Created at: April 9, 2026, 5:11 p.m.