Triple

T15141319
Position Surface form Disambiguated ID Type / Status
Subject Putussibau E361688 entity
Predicate servedBy P82 FINISHED
Object Pangsuma Airport E1144262 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: Pangsuma Airport | Statement: [Putussibau, servedBy, Pangsuma Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pangsuma Airport
Context triple: [Putussibau, servedBy, Pangsuma Airport]
  • A. Pangsuma Airport chosen
    Pangsuma Airport is a regional airport serving the town of Putussibau in West Kalimantan, Indonesia.
  • B. Mutiara Airport
    Mutiara Airport is a public airport in Palu, Central Sulawesi, Indonesia, serving as the main air gateway to the region.
  • C. Begumpet Airport
    Begumpet Airport is the former primary airport of Hyderabad, India, now used mainly for military, training, and charter operations after being superseded by Rajiv Gandhi International Airport.
  • D. Ranai Airport
    Ranai Airport is the main civil and military airport serving the remote Natuna Islands in Indonesia’s Riau Islands province.
  • E. Dumatubin Airport
    Dumatubin Airport is a small regional airport serving the Kai Islands in Indonesia, providing domestic air connections to this remote archipelago.
  • 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_69d85a0759908190b8a051d2e2a1cbe6 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e005c46a248190a2364092d40274f3 completed April 15, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5e4752481908e456279a1dfbff6 completed May 9, 2026, 7:44 a.m.
Created at: April 10, 2026, 3:07 a.m.