Triple

T4517633
Position Surface form Disambiguated ID Type / Status
Subject Travels in Tartary E103190 entity
Predicate author P4 FINISHED
Object Peter Fleming E18855 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: Peter Fleming | Statement: [Travels in Tartary, author, Peter Fleming]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Fleming
Context triple: [Travels in Tartary, author, Peter Fleming]
  • A. Peter Fleming chosen
    Peter Fleming was a British travel writer, journalist, and adventurer, best known for his travel books and for his work as a wartime intelligence officer.
  • B. William Fleming
    William Fleming is a relatively common personal name shared by multiple notable individuals across fields such as politics, education, and sports.
  • C. Ian Fleming
    Ian Fleming was a British author and journalist best known as the creator of the James Bond spy novels.
  • D. Walter Connolly
    Walter Connolly was an American character actor of the 1930s known for his comic and often blustery supporting roles in Hollywood films.
  • E. Robert Fairthorne
    Robert Fairthorne was a British information scientist and mathematician known for his influential work in documentation, information retrieval theory, and the early development of information science as a discipline.
  • 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_69bd43dba59881908cf59b31df8c7ae1 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd572933408190b67c4ef6a7babe75 completed March 20, 2026, 2:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfa0cf7bc81908fbe7f2e723a0784 completed March 21, 2026, 1:53 a.m.
Created at: March 20, 2026, 1:02 p.m.