Triple

T24736757
Position Surface form Disambiguated ID Type / Status
Subject Usmar Ismail E618435 entity
Predicate commemoratedBy P500 FINISHED
Object Usmar Ismail Film Center
The Usmar Ismail Film Center is a cultural venue and cinema in Jakarta dedicated to preserving and promoting Indonesian film heritage and honoring pioneering director Usmar Ismail.
E1647869 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: Usmar Ismail Film Center | Statement: [Usmar Ismail, commemoratedBy, Usmar Ismail Film Center]
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: Usmar Ismail Film Center
Triple: [Usmar Ismail, commemoratedBy, Usmar Ismail Film Center]
Generated description
The Usmar Ismail Film Center is a cultural venue and cinema in Jakarta dedicated to preserving and promoting Indonesian film heritage and honoring pioneering director Usmar Ismail.

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_69e2fab8f95c81908bb9e552cf3280c2 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f4103a15a48190afb94f6e4bb16b2a completed May 1, 2026, 2:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10102256148190a1beb8b77921d084 completed May 22, 2026, 8:13 a.m.
NEDg Description generation batch_6a10136e15dc81908478704742d7c95e completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10145c05c88190a29367197865506c completed May 22, 2026, 8:31 a.m.
Created at: April 18, 2026, 4:03 a.m.