Triple
T67837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eiffel Tower |
E1351
|
entity |
| Predicate | visitorsPerYear |
P427
|
FINISHED |
| Object | about 7 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: about 7 million | Statement: [Eiffel Tower, visitorsPerYear, about 7 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visitorsPerYear Context triple: [Eiffel Tower, visitorsPerYear, about 7 million]
-
A.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
B.
frequentlyVisitedBy
Indicates that an entity is regularly or often visited by another entity.
-
C.
visitorCenter
Indicates that a location serves as a visitor center for a place, providing information or services to visitors of that place.
-
D.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
E.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a2509b5a088190bb9d2b650aeb8bca |
completed | Feb. 28, 2026, 2:19 a.m. |
| PD | Predicate disambiguation | batch_69a24ea749788190bc17865171ff909a |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.