Triple

T1843887
Position Surface form Disambiguated ID Type / Status
Subject Kedah E41239 entity
Predicate borderedBy P224 FINISHED
Object Penang E55976 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: Penang | Statement: [Kedah, borderedBy, Penang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Penang
Context triple: [Kedah, borderedBy, Penang]
  • A. Penang chosen
    Penang is a Malaysian state and island renowned for its multicultural heritage, historic George Town, and vibrant street food scene.
  • B. Johor Bahru
    Johor Bahru is a large, rapidly developing city in southern Peninsular Malaysia, located just across the causeway from Singapore and serving as the capital of Johor state.
  • C. Kuantan
    Kuantan is a coastal city on the east coast of Peninsular Malaysia known as a major economic and cultural center and gateway to the South China Sea.
  • 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. Kota Kinabalu
    Kota Kinabalu is a coastal city in Malaysian Borneo known as the gateway to Mount Kinabalu and the biodiverse rainforests and marine parks of Sabah.
  • 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_69a88648cd44819093303206d96d76ad completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb04eb0748190b226f932e544925f completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6ae17a808190b5574ca6baafdb71 completed March 9, 2026, 6:38 a.m.
Created at: March 4, 2026, 7:33 p.m.