Triple
T38208707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SFR Group |
E1009276
|
entity |
| Predicate | hasMajorMarketPosition |
P1850
|
FINISHED |
| Object | one of the largest mobile operators 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: one of the largest mobile operators in France | Statement: [SFR Group, hasMajorMarketPosition, one of the largest mobile operators in France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMajorMarketPosition Context triple: [SFR Group, hasMajorMarketPosition, one of the largest mobile operators in France]
-
A.
hasMajorMarket
Indicates that an entity has a primary or most significant market in a specified location or segment.
-
B.
marketPosition
chosen
Indicates the relative standing or rank an entity holds within a specific market compared to its competitors.
-
C.
hasMarketRole
Indicates that an entity holds or performs a specific functional role within a market or marketplace context.
-
D.
hasMarketTier
Indicates the market segment or tier classification to which an entity (such as a product, service, or customer) belongs.
-
E.
hasMarketFocus
Indicates that an entity concentrates its business activities, products, or services on a particular market or customer segment.
- F. None of above.
Provenance (3 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_69f76dc94fcc8190bd2f55e81f9d6527 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:30 p.m.