Triple

T10161028
Position Surface form Disambiguated ID Type / Status
Subject Sultan Mahmud Airport E233889 entity
Predicate cityServed P82 FINISHED
Object Kuala Terengganu E280295 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: Kuala Terengganu | Statement: [Sultan Mahmud Airport, cityServed, Kuala Terengganu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kuala Terengganu
Context triple: [Sultan Mahmud Airport, cityServed, Kuala Terengganu]
  • A. Kuala Terengganu chosen
    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.
  • B. Kuala Perlis
    Kuala Perlis is a small coastal town in Malaysia known as a key ferry gateway to the resort island of Langkawi.
  • C. Kulim
    Kulim is a prominent town and industrial hub in the Malaysian state of Kedah, known for its high-tech manufacturing and proximity to Penang.
  • D. Kuala Kangsar
    Kuala Kangsar is a historic royal town in the Malaysian state of Perak, known as the traditional seat of the Perak Sultanate.
  • E. Kuala Pilah
    Kuala Pilah is a historic inland town in the Malaysian state of Negeri Sembilan, known for its traditional Minangkabau cultural heritage and role as an administrative and commercial center for the surrounding rural district.
  • 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_69ca848e80748190b91d1e04d35512c7 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cdec59b01081908be6ca37dc575465 completed April 2, 2026, 4:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d96b0e4eac8190af28db3d334852cb completed April 10, 2026, 9:26 p.m.
Created at: March 30, 2026, 9:09 p.m.