Triple

T16145329
Position Surface form Disambiguated ID Type / Status
Subject Hals E391765 entity
Predicate givenName P17 FINISHED
Object Frans E365435 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: Frans | Statement: [Hals, givenName, Frans]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frans
Context triple: [Hals, givenName, Frans]
  • A. Frans chosen
    Frans is a common Dutch given name, notably borne by politician Frans Timmermans, a prominent European Union figure and former European Commission executive vice president.
  • B. Francen
    Francen is a surname most notably associated with Victor Francen, a Belgian-born actor prominent in early 20th-century European and American cinema.
  • C. Frant
    Frant is a village and civil parish in East Sussex, England, known for its historic church, traditional village green, and rural Wealden countryside setting.
  • D. Fransat
    Fransat is a French free-to-air satellite television platform that provides access to the national digital terrestrial TV channels across France.
  • E. Holland
    Holland is a historic coastal region in the western Netherlands that became the political and economic heartland of the emerging Dutch state.
  • 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_69d87f1c65e48190aa2b4c472e9bafc4 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21d9376fc8190bd9ef586b00c1d3b completed April 17, 2026, 11:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff2b9322c8190a773681679f9ad79 completed May 10, 2026, 2:51 a.m.
Created at: April 10, 2026, 5:01 a.m.