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.