Triple

T3066552
Position Surface form Disambiguated ID Type / Status
Subject Shine E62116 entity
Predicate distributor P1951 FINISHED
Object Fine Line Features E116127 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: Fine Line Features | Statement: [Shine, distributor, Fine Line Features]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fine Line Features
Context triple: [Shine, distributor, Fine Line Features]
  • A. Fine Line Features chosen
    Fine Line Features was an American specialty film distribution company known for releasing independent, foreign, and art-house films in the 1990s and early 2000s.
  • B. Fine Line
    "Fine Line" is a song by Paul McCartney from his 2005 album *Chaos and Creation in the Backyard*, noted for its melodic piano-driven arrangement and reflective lyrics.
  • C. Fine Line (album)
    Fine Line is the critically acclaimed second studio album by English singer-songwriter Harry Styles, known for its blend of pop, rock, and psychedelic influences and introspective lyricism.
  • D. Black Lines
    Black Lines is an abstract painting by Wassily Kandinsky that exemplifies his pioneering use of bold linear forms and vibrant color to explore non-representational expression.
  • E. Mitre Line
    The Mitre Line is a major commuter rail service in the Buenos Aires metropolitan area that links the city center with several northern suburbs.
  • 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_69ad85793e5c8190a358049bc4a98d8c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada0fd87308190918e7b616f033faa completed March 8, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1ef16cf2881908265dfe8a1e3424d completed March 11, 2026, 10:39 p.m.
Created at: March 8, 2026, 3:02 p.m.