Triple

T6986768
Position Surface form Disambiguated ID Type / Status
Subject Soekarno–Hatta International Airport E161983 entity
Predicate servesCity P82 FINISHED
Object Jakarta E29483 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: Jakarta | Statement: [Soekarno–Hatta International Airport, servesCity, Jakarta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jakarta
Context triple: [Soekarno–Hatta International Airport, servesCity, Jakarta]
  • A. Jakarta chosen
    Jakarta is the bustling capital and largest city of Indonesia, serving as the country’s political, economic, and cultural center on the island of Java.
  • B. 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.
  • C. 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.
  • D. 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.
  • 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_69c68856d7808190ab33ee914640281b completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db93c0ac8190a752a633247bb439 completed March 27, 2026, 7:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c761cfda14819088e11889f0151a37 completed March 28, 2026, 5:06 a.m.
Created at: March 27, 2026, 2:32 p.m.