Triple
T5244444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theresienwiese |
E118422
|
entity |
| Predicate | typicalVisitorCountDuringOktoberfest |
P43282
|
FINISHED |
| Object | millions of visitors per year |
—
|
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: millions of visitors per year | Statement: [Theresienwiese, typicalVisitorCountDuringOktoberfest, millions of visitors per year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVisitorCountDuringOktoberfest Context triple: [Theresienwiese, typicalVisitorCountDuringOktoberfest, millions of visitors per year]
-
A.
typicalEventDay
Indicates the day on which an event is normally or most commonly held or occurs.
-
B.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
C.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
D.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
E.
guestCountApproximate
chosen
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
- 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_69bd4468aacc8190a8196f71855cdf4f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b4fa0ec8190bce3da09aa768726 |
completed | March 20, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69bd77c1397c8190a7fd844d7a396e54 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:49 p.m.