Triple
T15260713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bet365 |
E364765
|
entity |
| Predicate | hasRevenue |
P7218
|
FINISHED |
| Object | multi-billion GBP annual revenue |
—
|
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: multi-billion GBP annual revenue | Statement: [Bet365, hasRevenue, multi-billion GBP annual revenue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRevenue Context triple: [Bet365, hasRevenue, multi-billion GBP annual revenue]
-
A.
hasRevenueUnit
Indicates that an entity’s revenue is measured, reported, or associated in terms of a specified unit (e.g., currency or measurement unit).
-
B.
hasRevenueSystem
Indicates that one entity possesses, uses, or is associated with a particular revenue-generating system or mechanism.
-
C.
revenue
chosen
Indicates the amount of income generated by an entity from its business activities or operations over a specified period.
-
D.
usesRevenueModel
Indicates that one entity applies or operates according to a particular revenue model to generate income.
-
E.
hasBroadcastRevenueModel
Indicates that one entity uses or is associated with a particular revenue model based on broadcasting activities.
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0084e85a08190b8e63598b9f6a535 |
completed | April 15, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69deca8d1bd48190a4b94f29b425e335 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:14 a.m.