Triple

T25491990
Position Surface form Disambiguated ID Type / Status
Subject Films Division of India E638858 entity
Predicate hasPart P35 FINISHED
Object National Museum of Indian Cinema
The National Museum of Indian Cinema is a dedicated museum in Mumbai that showcases the history, evolution, and cultural impact of Indian cinema through exhibits, artifacts, and interactive displays.
E1682981 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: National Museum of Indian Cinema | Statement: [Films Division of India, hasPart, National Museum of Indian Cinema]
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: National Museum of Indian Cinema
Triple: [Films Division of India, hasPart, National Museum of Indian Cinema]
Generated description
The National Museum of Indian Cinema is a dedicated museum in Mumbai that showcases the history, evolution, and cultural impact of Indian cinema through exhibits, artifacts, and interactive displays.

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_69e75dbbd2a88190b70e1e645de14b9a completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f7a6493481908fccf217f6296b95 completed May 2, 2026, 1:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ad6dc00081909c613de143b0b372 completed May 22, 2026, 7:24 p.m.
NEDg Description generation batch_6a10ae2f577481909be995d38010dcf7 completed May 22, 2026, 7:27 p.m.
NED2 Entity disambiguation (via description) batch_6a10aef10de8819099e12e65f4f9e768 completed May 22, 2026, 7:30 p.m.
Created at: April 21, 2026, 2:38 p.m.