Triple

T13796476
Position Surface form Disambiguated ID Type / Status
Subject Django (1966 film) E331529 entity
Predicate soundMix P14839 FINISHED
Object Mono
Mono is a single-channel audio format commonly used in early film and television soundtracks before stereo became standard.
 E1062991 NE FINISHED

How this triple was built (4 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: Mono | Statement: [Django (1966 film), soundMix, Mono]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mono
Context triple: [Django (1966 film), soundMix, Mono]
  • A. Mono
    Mono is an open-source, cross-platform implementation of Microsoft's .NET framework that enables running .NET applications on multiple operating systems.
  • B. Mono
    Mono is a Native American language of the Numic branch of the Uto-Aztecan language family, traditionally spoken in parts of California.
  • C. Mono
    Mono is a Japanese post-rock band known for its expansive, cinematic instrumentals and emotionally intense live performances.
  • D. Monos
    Monos is a 2019 Colombian war drama film that follows a group of teenage guerrilla soldiers on a remote mountaintop as their mission and sanity unravel.
  • E. Monolitten
    Monolitten is a famous 14-meter-tall granite sculpture by Gustav Vigeland, carved from a single stone and standing as the central monument in Oslo’s Vigeland Park.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mono
Triple: [Django (1966 film), soundMix, Mono]
Generated description
Mono is a single-channel audio format commonly used in early film and television soundtracks before stereo became standard.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mono
Target entity description: Mono is a single-channel audio format commonly used in early film and television soundtracks before stereo became standard.
  • A. Mono
    Mono is an open-source, cross-platform implementation of Microsoft's .NET framework that enables running .NET applications on multiple operating systems.
  • B. Mono
    Mono is a Native American language of the Numic branch of the Uto-Aztecan language family, traditionally spoken in parts of California.
  • C. Mono
    Mono is a Japanese post-rock band known for its expansive, cinematic instrumentals and emotionally intense live performances.
  • D. Monos
    Monos is a 2019 Colombian war drama film that follows a group of teenage guerrilla soldiers on a remote mountaintop as their mission and sanity unravel.
  • E. Monolitten
    Monolitten is a famous 14-meter-tall granite sculpture by Gustav Vigeland, carved from a single stone and standing as the central monument in Oslo’s Vigeland Park.
  • F. None of above. chosen

Provenance (5 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_69d81c58feb08190a77bca8bf7d6d20f completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de025be1f08190aac525d72d7dc0c3 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b08508688190b7e8c33e6b65e25d completed May 3, 2026, 8:31 p.m.
NEDg Description generation batch_69f7b48bf704819098bfb70def28a0d1 completed May 3, 2026, 8:48 p.m.
NED2 Entity disambiguation (via description) batch_69f7b5c9e284819094e7af030e1e9034 completed May 3, 2026, 8:53 p.m.
Created at: April 9, 2026, 10:11 p.m.