Triple
T297866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Musée Mécanique |
E6131
|
entity |
| Predicate | tourismCategory |
P1769
|
FINISHED |
| Object | San Francisco tourist attraction |
—
|
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: San Francisco tourist attraction | Statement: [Musée Mécanique, tourismCategory, San Francisco tourist attraction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismCategory Context triple: [Musée Mécanique, tourismCategory, San Francisco tourist attraction]
-
A.
tourismType
chosen
Indicates the specific category or kind of tourism activity or experience associated with an entity.
-
B.
tourismRegion
Indicates that a place or area is designated or recognized as a tourism region associated with another geographic or administrative entity.
-
C.
touristAttractionIn
Indicates that a place functions as a tourist attraction located within a specified geographic area or entity.
-
D.
tourismImportance
Indicates the degree to which a place or entity is significant or valuable as a destination or attraction for tourists.
-
E.
isTouristDestination
Indicates that a place is recognized as a location people commonly visit for leisure, sightseeing, or travel.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea4778cc8190be7b648a82542891 |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e937af888190a0960708f09ae033 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.