Triple

T12338968
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Engineering, University of Indonesia E294168 entity
Predicate locatedIn P40 FINISHED
Object Depok E175029 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: Depok | Statement: [Faculty of Engineering, University of Indonesia, locatedIn, Depok]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Depok
Context triple: [Faculty of Engineering, University of Indonesia, locatedIn, Depok]
  • A. Depok chosen
    Depok is a rapidly growing commuter city in Indonesia located between Jakarta and Bogor, known for its universities and residential developments.
  • B. Bekasi
    Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
  • C. Bogor
    Bogor is a city on the Indonesian island of Java known for its cool climate, botanical gardens, and role as a major educational and research center.
  • D. Bogor Regency
    Bogor Regency is an administrative region in West Java, Indonesia, that encircles the city of Bogor and is known for its rapidly growing suburban and rural communities.
  • E. Tangerang
    Tangerang is a major urban and industrial city in Indonesia located just west of Jakarta on the island of Java.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ab6ccbec8190b09e2d357aa80064 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f678698819091462b44ff3435f6 completed April 10, 2026, 6:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f62aa7e33c819084e5673a5fb8cac7 completed May 2, 2026, 4:47 p.m.
Created at: April 8, 2026, 9:53 p.m.