Triple

T17306866
Position Surface form Disambiguated ID Type / Status
Subject Vezot Haberakhah E420185 entity
Predicate mentionsLocation P15985 FINISHED
Object Dan E72350 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: [Vezot Haberakhah, mentionsLocation, Dan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dan
Context triple: [Vezot Haberakhah, mentionsLocation, Dan]
  • A. Dan
    Dan was the personal name of Emperor Xizong, a 12th-century ruler of the Jurchen-led Jin dynasty in northern China.
  • B. Dan chosen
    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.
  • C. 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.
  • D. Dan
    Dan is a male given name commonly used in English-speaking countries, often as a short form of Daniel.
  • 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_69d889d22b848190a4663d0b8f8f76e7 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e4390005dc81908345ebb6dd970582 completed April 19, 2026, 2:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0180e0c1b881908aa2b6b4d8ac04b6 completed May 11, 2026, 7:10 a.m.
Created at: April 10, 2026, 5:43 a.m.