Triple

T11207974
Position Surface form Disambiguated ID Type / Status
Subject Kapas Island E265221 entity
Predicate nearestTown P350 FINISHED
Object Marang E912424 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: Marang | Statement: [Kapas Island, nearestTown, Marang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marang
Context triple: [Kapas Island, nearestTown, Marang]
  • A. Marang District chosen
    Marang District is a coastal administrative district in Terengganu, Malaysia, known for its fishing villages, beaches, and nearby resort islands.
  • B. Temerloh
    Temerloh is a town in central Pahang, Malaysia, known as a regional commercial hub and gateway to the state's interior.
  • C. Kuala Kangsar
    Kuala Kangsar is a historic royal town in the Malaysian state of Perak, known as the traditional seat of the Perak Sultanate.
  • D. Kuala Terengganu
    Kuala Terengganu is a coastal city in northeastern Peninsular Malaysia known for its Islamic heritage architecture, traditional Malay culture, and proximity to popular island destinations.
  • E. Ratekau
    Ratekau is a municipality in the district of Ostholstein in Schleswig-Holstein, northern Germany, near the Baltic Sea coast.
  • 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_69d6aac59460819089b9848b27f57848 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8d5f8908190903817f84c629ba1 completed April 9, 2026, 5:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4cc2ec5348190b66a3cc5779aa327 completed April 19, 2026, 12:35 p.m.
Created at: April 8, 2026, 9:30 p.m.