Triple

T23231173
Position Surface form Disambiguated ID Type / Status
Subject Lieutenant Thomas Glahn E581156 entity
Predicate firstAppearance P795 FINISHED
Object Pan NE NERFINISHED

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: Pan | Statement: [Lieutenant Thomas Glahn, firstAppearance, Pan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pan
Context triple: [Lieutenant Thomas Glahn, firstAppearance, Pan]
  • A. Pan
    Pan is the rustic Greek god of shepherds, flocks, and wild nature, often depicted with goat-like features and associated with music and untamed wilderness.
  • B. Pan
    Pan is a genus of great apes that includes chimpanzees and bonobos, our closest living evolutionary relatives.
  • C. Pan chosen
    Pan is a 1894 novel by Norwegian author Knut Hamsun, known for its lyrical portrayal of nature and its psychologically intense depiction of love and jealousy.
  • D. Pan
    Pan is a former member of the American rock band Black Veil Brides, known for their theatrical style and blend of glam metal and post-hardcore.
  • E. Pan
    Pan is a 2015 fantasy adventure film that serves as an origin story for J.M. Barrie’s Peter Pan, reimagining how the boy who wouldn’t grow up first came to Neverland.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e246043c48819089bae72c9a9c306c completed April 17, 2026, 2:39 p.m.
NER Named-entity recognition batch_69f19231ef908190a791b4967916a66f completed April 29, 2026, 5:08 a.m.
Created at: April 17, 2026, 4:09 p.m.