Triple

T13608794
Position Surface form Disambiguated ID Type / Status
Subject Sarah Hyland E325132 entity
Predicate film P9968 FINISHED
Object Geek Charming E1050573 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: Geek Charming | Statement: [Sarah Hyland, film, Geek Charming]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Geek Charming
Context triple: [Sarah Hyland, film, Geek Charming]
  • A. Geek Charming chosen
    Geek Charming is a Disney Channel original teen romantic comedy film about a popular girl and a film geek whose lives intersect through an unlikely documentary project.
  • B. Beauty and the Geek
    Beauty and the Geek is a reality television series that pairs socially awkward but intelligent men with socially adept women in a competition designed to challenge stereotypes and foster personal growth.
  • C. Geek in the Pink
    "Geek in the Pink" is a playful, funk-infused pop song by singer-songwriter Jason Mraz that showcases his rapid-fire wordplay and upbeat, quirky style.
  • D. Geeks Bearing Gifts
    Geeks Bearing Gifts is a book by computing pioneer Ted Nelson that reflects on the history, philosophy, and future of digital media and information technology.
  • E. Geeks Bearing Gifts
    Geeks Bearing Gifts is a media and journalism-focused book by Jeff Jarvis that explores how digital disruption is reshaping news and proposes new models for the future of journalism.
  • 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_69d80769eaf081909d82f44e484d6113 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb07f462c8190b5b5e115d550037f completed April 12, 2026, 2:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78ae56e2081909c0fd044ce3730a9 completed May 3, 2026, 5:50 p.m.
Created at: April 9, 2026, 9:50 p.m.