Triple

T20567715
Position Surface form Disambiguated ID Type / Status
Subject Speaker of the DPD E505006 entity
Predicate officeLocation P40 FINISHED
Object Senayan, Jakarta NE NERFINISHED

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: Senayan, Jakarta | Statement: [Speaker of the DPD, officeLocation, Senayan, Jakarta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Senayan, Jakarta
Context triple: [Speaker of the DPD, officeLocation, Senayan, Jakarta]
  • A. Senayan, Jakarta chosen
    Senayan, Jakarta is a central district in Indonesia’s capital city known for its major government buildings, sports complex, and commercial centers.
  • B. Cilandak
    Cilandak is a district in South Jakarta, Indonesia, known as a primarily residential and commercial area with several educational institutions and office complexes.
  • C. Parung Panjang
    Parung Panjang is a district in Bogor Regency, West Java, Indonesia, known as a growing suburban and industrial area on the outskirts of Greater Jakarta.
  • D. Cibinong
    Cibinong is an urban district in West Java, Indonesia, serving as an administrative and commercial hub within the Greater Jakarta metropolitan area.
  • E. Jagakarsa
    Jagakarsa is a district in the southern part of Jakarta, Indonesia, known for its relatively green residential areas and educational institutions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4b6587c8190aee63dc7cff244ea completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a7a3fdc08190a34dcf4c4e51f078 completed April 20, 2026, 10:24 p.m.
Created at: April 16, 2026, 11:39 a.m.