Triple

T7904477
Position Surface form Disambiguated ID Type / Status
Subject Lieutenant Dan Taylor E183537 entity
Predicate givenName P17 FINISHED
Object Dan E182921 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: Dan | Statement: [Lieutenant Dan Taylor, givenName, Dan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dan
Context triple: [Lieutenant Dan Taylor, givenName, Dan]
  • A. Dan
    Dan is the protagonist of Cory Doctorow's science fiction novel "Down and Out in the Magic Kingdom," a post-scarcity future resident of a reputation-based society centered around a Disney theme park.
  • B. Dan chosen
    Dan is a male given name commonly used in English-speaking countries, often as a short form of Daniel.
  • C. Dan
    Dan is a central character in Louisa May Alcott's novel "Jo's Boys," known for his rough past, adventurous spirit, and deep loyalty to the Bhaer family.
  • D. Dan
    Dan is a biblical figure recognized as one of the twelve sons of Jacob and the traditional ancestor of the Tribe of Dan in the Hebrew Bible.
  • E. Dan
    Dan is a character in the play "Clybourne Park," representing a contemporary figure who uncovers the neighborhood’s buried history and helps connect past events to present-day tensions.
  • 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_69ca828d13088190b222be7aa9f9315c completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3a4331cc8190b50301c78767a850 completed March 31, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5bc35cec8190bda3dfe7d8d4ed18 completed March 31, 2026, 5:29 a.m.
Created at: March 30, 2026, 5:02 p.m.