Triple

T5437
Position Surface form Disambiguated ID Type / Status
Subject Theodor Nelson E107 entity
Predicate givenName P17 FINISHED
Object Theodor E107 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: Theodor | Statement: [Theodor Nelson, givenName, Theodor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Theodor
Context triple: [Theodor Nelson, givenName, Theodor]
  • A. Theodor chosen
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • B. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • C. Andrei
    Andrei is a masculine given name commonly used in Slavic and Eastern European countries, equivalent to the English name Andrew.
  • D. Herbert
    Herbert is a masculine given name of Germanic origin that has been borne by various notable figures, including U.S. President Herbert Hoover.
  • E. Pierre
    Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
  • 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_69a238d6b47881909e68288aed2fd858 completed Feb. 28, 2026, 12:37 a.m.
NER Named-entity recognition batch_69a2399d5cf88190998f9b95c817a60f completed Feb. 28, 2026, 12:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69a25aac4900819093912edb0121ff9d completed Feb. 28, 2026, 3:02 a.m.
Created at: Feb. 28, 2026, 12:40 a.m.