Triple
T16907118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin Zoological Garden |
E424591
|
entity |
| Predicate | hasVisitorCountPerYear |
P427
|
FINISHED |
| Object | over 3 million |
—
|
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 3 million | Statement: [Berlin Zoological Garden, hasVisitorCountPerYear, over 3 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVisitorCountPerYear Context triple: [Berlin Zoological Garden, hasVisitorCountPerYear, over 3 million]
-
A.
visitYear
Indicates the specific year in which a visit or visitation event took place.
-
B.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
C.
annualVisitation
Indicates a recurring visit or attendance that takes place once every year between the related entities.
-
D.
hasVisitorsFrom
Indicates that an entity receives or has received visitors originating from another specified entity or location.
-
E.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3ca39f9b08190b15106c6caf895ec |
completed | April 18, 2026, 6:15 p.m. |
| PD | Predicate disambiguation | batch_69e32b9489408190bcb2ede567ff5bf9 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:30 a.m.