Triple

T10848201
Position Surface form Disambiguated ID Type / Status
Subject Eric Dane E256070 entity
Predicate portrayedIn P626 FINISHED
Object Red Hot
"Red Hot" is a 1993 drama film set in Soviet-era Latvia, following a group of young musicians who risk severe punishment to secretly play Western rock music.
 E889745 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: Red Hot | Statement: [Eric Dane, portrayedIn, Red Hot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Red Hot
Context triple: [Eric Dane, portrayedIn, Red Hot]
  • A. Red Hot & Boom
    Red Hot & Boom is a large annual Independence Day-themed fireworks and music festival held in Altamonte Springs, Florida.
  • B. Hot Hot Hot
    "Hot Hot Hot" is a song best known as the B-side to the English post-punk band The Cure’s single "The Walk."
  • C. The Hot Rock
    The Hot Rock is a 1972 comic heist film starring Robert Redford and George Segal, centered on a group of thieves repeatedly attempting to steal the same diamond.
  • D. Hot & Heavy
    "Hot & Heavy" is an introspective indie rock song by singer-songwriter Lucy Dacus, known for its nostalgic lyrics and emotionally resonant storytelling.
  • E. Red, Hot and Blue
    Red, Hot and Blue is a 1936 Broadway musical comedy with music and lyrics by Cole Porter, known for its witty songs and star-studded original cast.
  • 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: Red Hot
Triple: [Eric Dane, portrayedIn, Red Hot]
Generated description
"Red Hot" is a 1993 drama film set in Soviet-era Latvia, following a group of young musicians who risk severe punishment to secretly play Western rock music.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Red Hot
Target entity description: "Red Hot" is a 1993 drama film set in Soviet-era Latvia, following a group of young musicians who risk severe punishment to secretly play Western rock music.
  • A. Red Hot & Boom
    Red Hot & Boom is a large annual Independence Day-themed fireworks and music festival held in Altamonte Springs, Florida.
  • B. Hot Hot Hot
    "Hot Hot Hot" is a song best known as the B-side to the English post-punk band The Cure’s single "The Walk."
  • C. The Hot Rock
    The Hot Rock is a 1972 comic heist film starring Robert Redford and George Segal, centered on a group of thieves repeatedly attempting to steal the same diamond.
  • D. Hot & Heavy
    "Hot & Heavy" is an introspective indie rock song by singer-songwriter Lucy Dacus, known for its nostalgic lyrics and emotionally resonant storytelling.
  • E. Red, Hot and Blue
    Red, Hot and Blue is a 1936 Broadway musical comedy with music and lyrics by Cole Porter, known for its witty songs and star-studded original cast.
  • 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_69d6aa81a5d08190aa86689061d1ddd2 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d75114ca988190a0e730131adb2df0 completed April 9, 2026, 7:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69deb170e714819097babb2b850342d2 completed April 14, 2026, 9:28 p.m.
NEDg Description generation batch_69dec255abb08190bf93573c41aa35e9 completed April 14, 2026, 10:40 p.m.
NED2 Entity disambiguation (via description) batch_69dec7c48c3c81909365b901830f0906 completed April 14, 2026, 11:03 p.m.
Created at: April 8, 2026, 9:20 p.m.