Triple
T19896559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basohli school |
E478166
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Basohli |
—
|
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: Basohli | Statement: [Basohli school, namedAfter, Basohli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Basohli Context triple: [Basohli school, namedAfter, Basohli]
-
A.
Basohli
chosen
Basohli is a historic town in Jammu and Kashmir, India, renowned as the birthplace of the bold, vibrant Basohli school of Pahari miniature painting.
-
B.
Bijbehara
Bijbehara is a historic town in the Kashmir Valley of Jammu and Kashmir, India, known for its apple orchards and scenic location along the Jhelum River.
-
C.
Kasauli
Kasauli is a small, picturesque hill station in the Indian state of Himachal Pradesh, known for its colonial-era architecture, pine forests, and tranquil atmosphere.
-
D.
Chamkoria
Chamkoria is the former name of Borovets, one of Bulgaria’s oldest and most popular mountain ski resorts.
-
E.
Loharu
Loharu is a town in the Bhiwani district of Haryana, India, known for its railway junction and historical Loharu Fort.
- 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6593dba78819082c8b80e65246171 |
completed | April 20, 2026, 4:50 p.m. |
Created at: April 10, 2026, 1:52 p.m.