Triple

T3234166
Position Surface form Disambiguated ID Type / Status
Subject france.tv E67809 entity
Predicate replaces P101 FINISHED
Object Pluzz
Pluzz was France Télévisions’ former online catch-up TV and streaming platform, later succeeded by france.tv.
E339515 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: Pluzz | Statement: [france.tv, replaces, Pluzz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pluzz
Context triple: [france.tv, replaces, Pluzz]
  • A. Pengo
    Pengo is a Dravidian language spoken primarily by the Pengo people in parts of central India, especially in Odisha and neighboring regions.
  • B. Priller
    Priller is a German surname most notably associated with Josef Priller, a famous Luftwaffe fighter ace of World War II.
  • C. Tikkana
    Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
  • D. The Toy
    The Toy is a 1982 comedy film starring Richard Pryor as a man hired by a wealthy businessman to be a spoiled rich child's "live" plaything, exploring themes of race, class, and exploitation through slapstick humor.
  • E. Ecco
    Ecco is a literary imprint known for publishing high-quality fiction, nonfiction, and poetry under the HarperCollins umbrella.
  • 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: Pluzz
Triple: [france.tv, replaces, Pluzz]
Generated description
Pluzz was France Télévisions’ former online catch-up TV and streaming platform, later succeeded by france.tv.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Pluzz
Target entity description: Pluzz was France Télévisions’ former online catch-up TV and streaming platform, later succeeded by france.tv.
  • A. Pengo
    Pengo is a Dravidian language spoken primarily by the Pengo people in parts of central India, especially in Odisha and neighboring regions.
  • B. Priller
    Priller is a German surname most notably associated with Josef Priller, a famous Luftwaffe fighter ace of World War II.
  • C. Tikkana
    Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
  • D. The Toy
    The Toy is a 1982 comedy film starring Richard Pryor as a man hired by a wealthy businessman to be a spoiled rich child's "live" plaything, exploring themes of race, class, and exploitation through slapstick humor.
  • E. Ecco
    Ecco is a literary imprint known for publishing high-quality fiction, nonfiction, and poetry under the HarperCollins umbrella.
  • 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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaedcd9588190b3623f0109d653a4 completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b277404f6c8190803cf67cc8423430 completed March 12, 2026, 8:20 a.m.
NEDg Description generation batch_69b27844c6708190ac61f00a74a2ef27 completed March 12, 2026, 8:24 a.m.
NED2 Entity disambiguation (via description) batch_69b27911ff1481908a36f279a871c510 completed March 12, 2026, 8:28 a.m.
Created at: March 8, 2026, 3:08 p.m.