Triple

T16956234
Position Surface form Disambiguated ID Type / Status
Subject György E411309 entity
Predicate relatedName P3889 FINISHED
Object Georges E18182 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: Georges | Statement: [György, relatedName, Georges]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Georges
Context triple: [György, relatedName, Georges]
  • A. Georges chosen
    Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
  • B. Jacques
    Jacques is the French form of the given name James, commonly used in French-speaking countries.
  • C. Pierre
    Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
  • D. Eugène
    Eugène is a masculine given name of French origin, derived from the Greek "Eugenios," meaning "well-born" or "noble."
  • E. Théodore
    Théodore is a masculine given name of Greek origin, commonly used in French-speaking countries and borne by notable figures such as the Reformation theologian Théodore Beza.
  • 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_69d886c9c9d481909afe222093641cae completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d01c7b408190b5a2b6c62b050b76 completed April 18, 2026, 6:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d4666f14819095fc3bcf5e459b61 completed May 10, 2026, 6:54 p.m.
Created at: April 10, 2026, 5:31 a.m.