Triple

T37314349
Position Surface form Disambiguated ID Type / Status
Subject Chief Minister of Madhya Pradesh E926292 entity
Predicate officeHolders P9949 FINISHED
Object Govind Narayan Singh
Govind Narayan Singh was an Indian politician who served as a prominent leader in Madhya Pradesh politics, including a term as the state's chief minister in the 1960s.
E2261577 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: Govind Narayan Singh | Statement: [Chief Minister of Madhya Pradesh, officeHolders, Govind Narayan Singh]
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: Govind Narayan Singh
Triple: [Chief Minister of Madhya Pradesh, officeHolders, Govind Narayan Singh]
Generated description
Govind Narayan Singh was an Indian politician who served as a prominent leader in Madhya Pradesh politics, including a term as the state's chief minister in the 1960s.

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_69f76eb28af88190b093b32e3fd614ab completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb5b3a204081908b3e379d9dc8d271 completed May 6, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41852434148190ae0f95949e35db48 completed June 28, 2026, 8:33 p.m.
NEDg Description generation batch_6a4189f025b881909ae2941adacb81d4 completed June 28, 2026, 8:54 p.m.
NED2 Entity disambiguation (via description) batch_6a418a6539108190ad85de6e8da19d8f completed June 28, 2026, 8:56 p.m.
Created at: May 3, 2026, 4:16 p.m.