Triple

T22122873
Position Surface form Disambiguated ID Type / Status
Subject Anne of Green Gables (1934 film) E546715 entity
Predicate stars P1956 FINISHED
Object Sara Haden NE NERFINISHED

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: Sara Haden | Statement: [Anne of Green Gables (1934 film), stars, Sara Haden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sara Haden
Context triple: [Anne of Green Gables (1934 film), stars, Sara Haden]
  • A. Sara Haden chosen
    Sara Haden was an American character actress best known for her supporting roles in classic Hollywood films of the 1930s and 1940s, including several entries in the Andy Hardy series.
  • B. Sara Braun
    Sara Braun was a prominent late 19th- and early 20th-century businesswoman and philanthropist in Chilean Patagonia, known for her influential role in regional development and society.
  • C. Milynn Sarley
    Milynn Sarley is an American actress and internet personality known for her roles in low-budget fantasy and action films as well as her presence in online geek and gaming communities.
  • D. Sara Harmon
    Sara Harmon is the mother of Lucy Harmon.
  • E. Sarah Gerhardt
    Sarah Gerhardt is an American big-wave surfer best known as the first woman to surf the notoriously dangerous Mavericks break in Northern California.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e38b3848190ac3a4fa97d56e65a completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1297f3fb48190b6aaca18b40c37ab completed April 28, 2026, 9:41 p.m.
Created at: April 16, 2026, 8:31 p.m.