Triple

T23801176
Position Surface form Disambiguated ID Type / Status
Subject B. G. Kher E588676 entity
Predicate fullName P16 FINISHED
Object Balasaheb Gangadhar Kher
Balasaheb Gangadhar Kher was an Indian lawyer, freedom fighter, and prominent Congress leader who served as the first Chief Minister of the Bombay State in independent India.
E1604486 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: Balasaheb Gangadhar Kher | Statement: [B. G. Kher, fullName, Balasaheb Gangadhar Kher]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Balasaheb Gangadhar Kher
Triple: [B. G. Kher, fullName, Balasaheb Gangadhar Kher]
Generated description
Balasaheb Gangadhar Kher was an Indian lawyer, freedom fighter, and prominent Congress leader who served as the first Chief Minister of the Bombay State in independent India.

Provenance (5 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_69e25d15db58819092ac1e6791696fd9 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1c74e430481909debd10c71785912 completed April 29, 2026, 8:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f69783c0c819085bacc1467c6696b completed May 21, 2026, 8:22 p.m.
NEDg Description generation batch_6a0f6d3de27c8190b3cab02a1dfce6ae completed May 21, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_6a0f6e22305081909ad33dfaf65f004e completed May 21, 2026, 8:42 p.m.
Created at: April 17, 2026, 7:53 p.m.