Triple

T6780493
Position Surface form Disambiguated ID Type / Status
Subject Tanah Kusir Cemetery E155667 entity
Predicate locatedNear P294 FINISHED
Object Bintaro area E595417 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: Bintaro area | Statement: [Tanah Kusir Cemetery, locatedNear, Bintaro area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bintaro area
Context triple: [Tanah Kusir Cemetery, locatedNear, Bintaro area]
  • A. South Tangerang chosen
    South Tangerang is a rapidly developing satellite city in Indonesia’s Banten province, known as a residential and commercial hub within the Jakarta metropolitan area.
  • B. Cipinang
    Cipinang is a neighborhood in East Jakarta, Indonesia, known for housing one of the country’s main prisons and various urban residential and commercial areas.
  • C. Betawi
    Betawi is an Austronesian language spoken primarily by the Betawi people in and around Jakarta, Indonesia, and is closely associated with the city's urban culture and history.
  • D. Cipatat
    Cipatat is a district-level administrative area located within West Bandung Regency in West Java, Indonesia.
  • E. Senayan, Jakarta
    Senayan, Jakarta is a central district in Indonesia’s capital city known for its major government buildings, sports complex, and commercial centers.
  • 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_69c688162bf8819088b664b5c3b5be7a completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d26b32c0819093f86b1002260660 completed March 27, 2026, 6:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712d27a388190ab44e6e754019fca completed March 27, 2026, 11:29 p.m.
Created at: March 27, 2026, 2:14 p.m.