Triple
T17049384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ultra Europe |
E413652
|
entity |
| Predicate | approximateTotalAttendance |
P86916
|
FINISHED |
| Object | over 100,000 attendees per edition |
—
|
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: over 100,000 attendees per edition | Statement: [Ultra Europe, approximateTotalAttendance, over 100,000 attendees per edition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateTotalAttendance Context triple: [Ultra Europe, approximateTotalAttendance, over 100,000 attendees per edition]
-
A.
estimatedCumulativeAttendance
chosen
Indicates the total number of attendees expected to have been present over a period of time, aggregated up to a given point.
-
B.
hasApproximateNumberOfPeople
Indicates that an entity is associated with an estimated or approximate count of people, rather than an exact number.
-
C.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
-
D.
grandFinalAttendance
Indicates the number of people who attended the grand final event.
-
E.
fanAttendance
Indicates the number or presence of fans attending an event, such as a game, show, or performance.
- 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_69d886cde3d481908d4d01ba88ba7eb7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3daa092f08190a9e37404a9de662c |
completed | April 18, 2026, 7:25 p.m. |
| PD | Predicate disambiguation | batch_69e35d60a588819084f53ef9f8b2e7c0 |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:34 a.m.