Triple
T22059834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2011–2012 Leonardo da Vinci exhibition at the National Gallery, London |
E545121
|
entity |
| Predicate | visitorDemand |
P146466
|
FINISHED |
| Object | very high |
—
|
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: very high | Statement: [2011–2012 Leonardo da Vinci exhibition at the National Gallery, London, visitorDemand, very high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visitorDemand Context triple: [2011–2012 Leonardo da Vinci exhibition at the National Gallery, London, visitorDemand, very high]
-
A.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
B.
visitorCount
Indicates the number of visitors associated with a particular entity, context, or time period.
-
C.
visitorService
Indicates a relationship where a service is provided specifically for or to visitors, such as assistance, information, or support during their visit.
-
D.
visitorInformation
Indicates that information or details are provided for or about visitors in relation to a particular place, service, or event.
-
E.
visitorUse
Indicates that an entity is being used, accessed, or engaged with by a visitor or temporary user.
- F. None of above. chosen
Provenance (4 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_69e11e3377c48190890c17407b9527d6 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1285ba1c88190b4bc0c73f3cf04e1 |
completed | April 28, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69e6f643ca74819083e8ab78e843f243 |
completed | April 21, 2026, 4 a.m. |
| PDg | Predicate description generation | batch_69e6fad4a540819096cdd5ea08527220 |
completed | April 21, 2026, 4:19 a.m. |
Created at: April 16, 2026, 8:27 p.m.