Triple

T6940349
Position Surface form Disambiguated ID Type / Status
Subject Bekasi Station E160656 entity
Predicate hasNearbyLandmark P2064 FINISHED
Object Bekasi city center E172668 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: Bekasi city center | Statement: [Bekasi Station, hasNearbyLandmark, Bekasi city center]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bekasi city center
Context triple: [Bekasi Station, hasNearbyLandmark, Bekasi city center]
  • A. Bekasi chosen
    Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
  • B. Central Jakarta
    Central Jakarta is the administrative and political heart of Indonesia’s capital city, encompassing key government institutions, historic landmarks, and major commercial districts.
  • C. West Jakarta
    West Jakarta is a densely populated administrative city of Jakarta, Indonesia, known for its mix of residential areas, commercial centers, and historical sites.
  • D. East Jakarta
    East Jakarta is one of the administrative cities of Indonesia’s capital, Jakarta, known for its mix of residential areas, industrial zones, and transportation hubs.
  • E. North Jakarta
    North Jakarta is a coastal administrative city of Indonesia’s capital region, known for its busy port, industrial zones, and historic waterfront areas.
  • 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_69c6884f3db4819080ad65da69386206 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6da641ce08190a133c9ba4977755d completed March 27, 2026, 7:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7515880948190970cadd7adeda435 completed March 28, 2026, 3:56 a.m.
Created at: March 27, 2026, 2:28 p.m.