Triple

T1660612
Position Surface form Disambiguated ID Type / Status
Subject Helen Garner E35895 entity
Predicate name P16 FINISHED
Object Helen Garner E35895 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: Helen Garner | Statement: [Helen Garner, name, Helen Garner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen Garner
Context triple: [Helen Garner, name, Helen Garner]
  • A. Helen Garner chosen
    Helen Garner is an acclaimed Australian novelist, short story writer, and journalist known for her incisive explorations of everyday life, relationships, and moral complexity.
  • B. Anita Heiss
    Anita Heiss is an Australian Wiradjuri author, academic, and advocate known for her contributions to Indigenous literature and commentary on Aboriginal identity and representation.
  • C. Lucinda Riley
    Lucinda Riley was a bestselling Irish author best known for her multi-volume historical fiction series "The Seven Sisters," which achieved international acclaim.
  • D. Philip Hensher
    Philip Hensher is a British novelist, critic, and academic known for works such as "The Northern Clemency" and his contributions to contemporary English literature.
  • E. Andrea Barrett
    Andrea Barrett is an American novelist and short story writer best known for her historically rich, science-infused fiction, including the National Book Award–winning collection "Ship Fever."
  • 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_69a88606aa808190aa0b421b4271f220 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90ab2a3488190a67c110a70d652c9 completed March 5, 2026, 4:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad682ab8a08190bdb33d79d5083029 completed March 8, 2026, 12:14 p.m.
Created at: March 4, 2026, 7:29 p.m.