Triple

T912480
Position Surface form Disambiguated ID Type / Status
Subject Frederick Banting E19691 entity
Predicate familyName P18 FINISHED
Object Banting E19691 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: Banting | Statement: [Frederick Banting, familyName, Banting]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Banting
Context triple: [Frederick Banting, familyName, Banting]
  • A. Banting chosen
    Banting is a surname most famously associated with Frederick Banting, the Canadian physician and Nobel laureate who co-discovered insulin.
  • B. Malang
    Malang is a major city in East Java, Indonesia, known for its cool climate, colonial-era architecture, and proximity to popular mountain and volcanic tourist destinations.
  • C. Tasikmalaya
    Tasikmalaya is a significant city in West Java, Indonesia, known as an important cultural and economic hub for the Sundanese people.
  • D. Cirebon
    Cirebon is a coastal city in West Java, Indonesia, known as a cultural crossroads blending Sundanese and Javanese influences and serving as a significant regional trading and urban center.
  • E. Surabaya
    Surabaya is Indonesia’s second-largest city and a key commercial and industrial hub on the island of Java, historically serving as one of the region’s most important seaports.
  • 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_69a4939f91a08190ba68c2c81eab90fe completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b2df9ba88190824437026796586f completed March 1, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7cf5e59588190b9d2bb00adf5c871 completed March 4, 2026, 6:21 a.m.
Created at: March 1, 2026, 7:39 p.m.