Triple
T107579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Champs-Élysées |
E2172
|
entity |
| Predicate | tourismImportance |
P7889
|
FINISHED |
| Object | one of the most visited avenues in the world |
—
|
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: one of the most visited avenues in the world | Statement: [Champs-Élysées, tourismImportance, one of the most visited avenues in the world]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismImportance Context triple: [Champs-Élysées, tourismImportance, one of the most visited avenues in the world]
-
A.
tourismType
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.
isTouristDestination
Indicates that a place is recognized as a location people commonly visit for leisure, sightseeing, or travel.
-
D.
hasCulturalSignificanceFor
Indicates that something holds particular cultural meaning, value, or importance for a specified group or community.
-
E.
explores
Indicates actively investigating, traveling through, or examining something in order to discover or learn more about it.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25a1199ac8190ac65ffaaf45b4f5b |
completed | Feb. 28, 2026, 2:59 a.m. |
| PD | Predicate disambiguation | batch_69a2563e7188819091e9a94e071991d7 |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a25a10d6448190bee47847d5c13b84 |
completed | Feb. 28, 2026, 2:59 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.