Triple
T15095952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Live Nation Entertainment (stake) |
E360536
|
entity |
| Predicate | relatedBusinessModel |
P15707
|
FINISHED |
| Object | concert promotion |
—
|
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: concert promotion | Statement: [Live Nation Entertainment (stake), relatedBusinessModel, concert promotion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedBusinessModel Context triple: [Live Nation Entertainment (stake), relatedBusinessModel, concert promotion]
-
A.
associatedCompanyBusinessModel
chosen
Indicates that there is a relationship linking a company to the specific business model it follows or employs.
-
B.
relatedCompanyType
Indicates the type or nature of the relationship that one company has to another company.
-
C.
relatedMarket
Indicates that two markets are connected or associated, such that activity, conditions, or changes in one market are relevant to or influence the other.
-
D.
relatedService
Indicates that one service is connected or associated with another service in a relevant or dependent way.
-
E.
laterBusinessModel
Indicates that one business model occurs or is adopted after another in time, representing a subsequent or successor business model in a sequence.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005466e9c8190a68e1fbeb8922b1a |
completed | April 15, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:04 a.m.