Triple

T6982032
Position Surface form Disambiguated ID Type / Status
Subject Mikołaj E161869 entity
Predicate equivalentNameInEnglish P3437 FINISHED
Object Nicholas E28979 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: Nicholas | Statement: [Mikołaj, equivalentNameInEnglish, Nicholas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nicholas
Context triple: [Mikołaj, equivalentNameInEnglish, Nicholas]
  • A. Nicholas chosen
    Nicholas is a masculine given name of Greek origin, commonly used in many cultures and historically borne by numerous saints, rulers, and notable figures.
  • B. Nicholas Herrick
    Nicholas Herrick was the father of the 17th-century English lyric poet and cleric Robert Herrick.
  • C. Rupert
    Rupert is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by various notable figures.
  • D. Rupert
    Rupert is a small town located in Greenbrier County in the state of West Virginia, United States.
  • E. Nicholas Van Orton
    Nicholas Van Orton is a wealthy, emotionally detached investment banker whose life unravels after he becomes entangled in a mysterious and elaborate psychological "game" in the film *The Game*.
  • 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_69c68855dc0481909b4c7e9e9ed273db completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db6d3f3c8190b0121f7934440c34 completed March 27, 2026, 7:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7754c817c8190a8b17a5e2c4f1b05 completed March 28, 2026, 6:29 a.m.
Created at: March 27, 2026, 2:31 p.m.