Triple

T14263084
Position Surface form Disambiguated ID Type / Status
Subject DS567 E353572 entity
Predicate thirdParty P6378 FINISHED
Object Singapore E3670 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: Singapore | Statement: [DS567, thirdParty, Singapore]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Singapore
Context triple: [DS567, thirdParty, Singapore]
  • A. Singapore chosen
    Singapore is a sovereign city-state and island nation in Southeast Asia known for its global financial hub status, multicultural society, and highly developed, efficient infrastructure.
  • B. Singapore Bar
    The Singapore Bar is the professional body that regulates and represents qualified advocates and solicitors practicing law in Singapore.
  • C. Tuas, Singapore
    Tuas, Singapore is an industrial and port district at the western tip of Singapore, known for its heavy industries, container terminals, and land reclamation projects.
  • D. Sembawang, Singapore
    Sembawang, Singapore is a northern residential and industrial planning area known for its naval base heritage, waterfront park, and proximity to the Johor Strait.
  • E. Mandai, Singapore
    Mandai, Singapore is a planning area in the northern region of Singapore known for its nature reserves, wildlife attractions, and military training grounds.
  • 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_69d8278c43e08190824146f4632b89a5 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de63563fc88190b0abdbf8529c65eb completed April 14, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd326367348190b4b31b32f4ca5639 completed May 8, 2026, 12:46 a.m.
Created at: April 10, 2026, 1:09 a.m.