Triple
T25834585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baselworld |
E650760
|
entity |
| Predicate | peakVisitorNumbers |
P427
|
FINISHED |
| Object | over 100000 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 100000 per edition | Statement: [Baselworld, peakVisitorNumbers, over 100000 per edition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakVisitorNumbers Context triple: [Baselworld, peakVisitorNumbers, over 100000 per edition]
-
A.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
B.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
C.
peakDayAttendance
Indicates the number of attendees present on the single highest-attendance day within a given period or event.
-
D.
touristTraffic
Indicates the level, flow, or intensity of tourists visiting or moving through a particular place or area.
-
E.
typicalVisitorsPerSeason
Indicates the usual number of visitors associated with each season for a given entity or location.
- 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_69e7ab37438081908f1ccf6284839520 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f601f485c48190bdbeb260653d9849 |
completed | May 2, 2026, 1:53 p.m. |
| PD | Predicate disambiguation | batch_69f4938b960081909b53c074a3e0c7c2 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 7:41 a.m.