Triple

T6746152
Position Surface form Disambiguated ID Type / Status
Subject Dan Morgenstern E154218 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: [Dan Morgenstern, givenName, Dan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dan
Context triple: [Dan Morgenstern, 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_69c6880ef37881909268a5a7299b9293 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1b8a0f0819086b802983e8ffcb6 completed March 27, 2026, 6:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c71a76f0c8819097e19e016988f0b4 completed March 28, 2026, 12:01 a.m.
Created at: March 27, 2026, 2:10 p.m.