Triple

T10103437
Position Surface form Disambiguated ID Type / Status
Subject Segamat E216257 entity
Predicate hasNearbyLocality P3883 FINISHED
Object Kluang E232783 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: Kluang | Statement: [Segamat, hasNearbyLocality, Kluang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kluang
Context triple: [Segamat, hasNearbyLocality, Kluang]
  • A. Kluang chosen
    Kluang is a town and district capital located in the central part of Johor, Malaysia, known for its coffee culture and surrounding agricultural areas.
  • B. Kuala Kangsar
    Kuala Kangsar is a historic royal town in the Malaysian state of Perak, known as the traditional seat of the Perak Sultanate.
  • C. Batu Pahat
    Batu Pahat is a coastal town and important commercial and industrial hub in the Malaysian state of Johor.
  • D. 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.
  • E. Ipoh
    Ipoh is a prominent city in northwestern Peninsular Malaysia, known for its colonial-era architecture, limestone hills and caves, and vibrant food scene.
  • 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_69ca83d039f08190b9d10363221c69fb completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cdd09af07c819099774af46ebf62d7 completed April 2, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b6dcca848190851f6f1968fe244c completed April 5, 2026, 7:24 p.m.
Created at: March 30, 2026, 9:03 p.m.