Triple
T694552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Notre-Dame Basilica |
E13866
|
entity |
| Predicate | visitorNumbers |
P427
|
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: [Notre-Dame Basilica, visitorNumbers, millions of visitors per year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visitorNumbers Context triple: [Notre-Dame Basilica, visitorNumbers, millions of visitors per year]
-
A.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
B.
visitorCenter
Indicates that a location serves as a visitor center for a place, providing information or services to visitors of that place.
-
C.
frequentlyVisitedBy
Indicates that an entity is regularly or often visited by another entity.
-
D.
sessionCount
Indicates the number of distinct sessions associated with an entity or interaction context.
-
E.
visitedBy
Indicates that a location or entity is the destination or target of a visit performed by another entity.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c3f39c8190a3014df428817492 |
completed | March 1, 2026, 8:25 p.m. |
| PD | Predicate disambiguation | batch_69a49d23e0a08190b08be9d1eff2a1bb |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.