Triple

T14902571
Position Surface form Disambiguated ID Type / Status
Subject Hendrika E360042 entity
Predicate hasCognate P2525 FINISHED
Object Henriette E1038067 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: Henriette | Statement: [Hendrika, hasCognate, Henriette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Henriette
Context triple: [Hendrika, hasCognate, Henriette]
  • A. Henriette
    Henriette is the given first name of the French photographer and painter Dora Maar, renowned for her association with Pablo Picasso and the Surrealist movement.
  • B. Henriette chosen
    Henriette is a feminine given name of French origin, historically borne by various European women of note.
  • C. Henriette
    Henriette is a cartoon cat character from the Looney Tunes short "Odor-able Kitty," known for being the object of Pepé Le Pew’s misguided romantic pursuits.
  • D. Henrietta
    Henrietta is a suburban community in western New York State, located near Rochester within the Rust Belt region along the Interstate 90 corridor.
  • E. Henrietta
    Henrietta is a feminine given name of English origin, historically popular in the 18th and 19th centuries and borne by several notable figures.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded60b24008190bd272c0d61329400 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe968a17188190bced83ed1006e020 completed May 9, 2026, 2:06 a.m.
Created at: April 10, 2026, 2:11 a.m.