Triple
T3234079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TF1 Group |
E67807
|
entity |
| Predicate | hasAudienceShareRank |
P47497
|
FINISHED |
| Object | leading television group in France |
—
|
LITERAL FINISHED |
How this triple was built (2 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: leading television group in France | Statement: [TF1 Group, hasAudienceShareRank, leading television group in France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAudienceShareRank Context triple: [TF1 Group, hasAudienceShareRank, leading television group in France]
-
A.
hasAudience
Indicates that an entity is intended to be received, viewed, or engaged with by a particular group of people.
-
B.
hasAudienceSize
Indicates the relationship between an entity and the number of people or size of group that receives, views, or engages with it.
-
C.
sharesStatusWith
Indicates that two entities have the same status or state within a given context.
-
D.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
-
E.
hasAudienceReception
Indicates the relationship between a work or event and how it is received, perceived, or evaluated by its audience.
- F. None of above. chosen
Provenance (4 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. |
| PD | Predicate disambiguation | batch_69ada4159e0481908cbbdd750f5e08c7 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada698eeb48190a1f5762fdd3b7b63 |
completed | March 8, 2026, 4:40 p.m. |
Created at: March 8, 2026, 3:08 p.m.