Triple

T11422660
Position Surface form Disambiguated ID Type / Status
Subject Ampang Jaya E270663 entity
Predicate state P87 FINISHED
Object Selangor E39832 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: Selangor | Statement: [Ampang Jaya, state, Selangor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Selangor
Context triple: [Ampang Jaya, state, Selangor]
  • A. Selangor chosen
    Selangor is a highly developed and populous state on the west coast of Peninsular Malaysia that surrounds the federal territories of Kuala Lumpur and Putrajaya.
  • B. Pahang
    Pahang is a large Malaysian state on the eastern coast of Peninsular Malaysia, known for its extensive rainforests, highlands like Cameron Highlands, and long South China Sea coastline.
  • C. Johor
    Johor is a state in southern Peninsular Malaysia known for its strategic location bordering Singapore, diverse economy, and rich Malay cultural heritage.
  • D. Perak
    Perak is a Malaysian state on the west coast of the Malay Peninsula, historically known for its rich tin deposits and former status as a key sultanate within British Malaya.
  • E. Kedah
    Kedah is a state in northwestern Peninsular Malaysia, historically significant as one of the oldest Malay kingdoms and once part of British Malaya.
  • 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_69d6aadeef688190874bcecd88b3dd9b completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d801b357e88190ace56d36a945688f completed April 9, 2026, 7:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa92635b08190a333702bec5b94e9 completed May 9, 2026, 9:37 p.m.
Created at: April 8, 2026, 9:34 p.m.