Triple
T20351406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khandhar |
E496018
|
entity |
| Predicate | editingBy |
P1954
|
FINISHED |
| Object | Ramesh Joshi |
—
|
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: Ramesh Joshi | Statement: [Khandhar, editingBy, Ramesh Joshi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ramesh Joshi Context triple: [Khandhar, editingBy, Ramesh Joshi]
-
A.
Ramesh Joshi
chosen
Ramesh Joshi is a film editor known for his work on the Indian movie "Meghe Dhaka Tara."
-
B.
Rajesh Joshi
Rajesh Joshi is an Indian actor best known for his supporting roles in Hindi films during the 1990s.
-
C.
Vijay Joshi
Vijay Joshi is an Indian economist known for his influential work on macroeconomic policy and development, particularly in the context of the Indian economy.
-
D.
Aravind Joshi
Aravind Joshi was an Indian-American computer scientist and computational linguist known for pioneering work in formal grammar formalisms, particularly Tree Adjoining Grammars, and for foundational contributions to natural language processing.
-
E.
Dinesh Gupta
Dinesh Gupta was an Indian revolutionary freedom fighter known for his role in the anti-colonial struggle against British rule in Bengal.
- 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6784f8ff48190a070888786f6a989 |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:24 a.m.