Triple

T2008726
Position Surface form Disambiguated ID Type / Status
Subject Mary Louise Streep E43643 entity
Predicate middleName P143 FINISHED
Object Louise E5411 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: Louise | Statement: [Mary Louise Streep, middleName, Louise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Louise
Context triple: [Mary Louise Streep, middleName, Louise]
  • A. Louise chosen
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • B. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • C. Emilie
    Emilie is a young French girl in Michael Morpurgo’s novel and its film adaptation "War Horse," who befriends and cares for the horses Joey and Topthorn during World War I.
  • D. Madeleine
    Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
  • E. Jeanne
    Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
  • 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_69a88716e9f08190946313fdc949e3cf completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb89be08c81909eb5ea672ea46b2b completed March 7, 2026, 5:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea84704a08190ac2ddf0370587f14 completed March 9, 2026, 11 a.m.
Created at: March 4, 2026, 7:37 p.m.