Triple

T10209851
Position Surface form Disambiguated ID Type / Status
Subject RTL Group E242297 entity
Predicate ownsTVChannel P61714 FINISHED
Object 6ter
6ter is a French free-to-air television channel offering family-oriented entertainment, series, and films, and is part of the RTL Group portfolio.
 E849563 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: 6ter | Statement: [RTL Group, ownsTVChannel, 6ter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 6ter
Context triple: [RTL Group, ownsTVChannel, 6ter]
  • A. Terskol
    Terskol is a small settlement in Russia’s North Caucasus region, known as a gateway to Mount Elbrus and a base for mountain tourism and alpine research.
  • B. T5
    T5 is one of the lines of the Athens tram system, providing light-rail transit service along part of the city’s coastal and urban corridor.
  • C. T5
    T5 is a Transformer-based text-to-text language model developed by Google that treats every NLP task as converting input text to output text.
  • D. T5
    T5 is a major passenger terminal at London Heathrow Airport, primarily serving British Airways and Iberia flights.
  • E. T5
    T5 is a former passenger terminal of Berlin Brandenburg Airport that handled commercial air traffic before being closed to operations.
  • 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: 6ter
Triple: [RTL Group, ownsTVChannel, 6ter]
Generated description
6ter is a French free-to-air television channel offering family-oriented entertainment, series, and films, and is part of the RTL Group portfolio.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 6ter
Target entity description: 6ter is a French free-to-air television channel offering family-oriented entertainment, series, and films, and is part of the RTL Group portfolio.
  • A. Terskol
    Terskol is a small settlement in Russia’s North Caucasus region, known as a gateway to Mount Elbrus and a base for mountain tourism and alpine research.
  • B. T5
    T5 is a tram line of the Trambesòs light rail network serving the Barcelona metropolitan area.
  • C. T5
    T5 is one of the lines of the Athens tram system, providing light-rail transit service along part of the city’s coastal and urban corridor.
  • D. T5
    T5 is a major passenger terminal at London Heathrow Airport, primarily serving British Airways and Iberia flights.
  • E. T5
    T5 is a former passenger terminal of Berlin Brandenburg Airport that handled commercial air traffic before being closed to operations.
  • 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_69d381ae26c48190985abd0e25ee5d04 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d1f860048190bb20f7d3bf87f347 completed April 7, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69d652cca9c081909f705365c70db009 completed April 8, 2026, 1:06 p.m.
NEDg Description generation batch_69d654ddaed88190bcd7f1a2ee9dd462 completed April 8, 2026, 1:15 p.m.
NED2 Entity disambiguation (via description) batch_69d655338cc08190ba00163f0afa4c3b completed April 8, 2026, 1:16 p.m.
Created at: April 6, 2026, 11 a.m.