Triple
T4733322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Ville Rose |
E105062
|
entity |
| Predicate | usedInMarketing |
P25490
|
FINISHED |
| Object | promotion of Toulouse as a tourist destination |
—
|
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: promotion of Toulouse as a tourist destination | Statement: [La Ville Rose, usedInMarketing, promotion of Toulouse as a tourist destination]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedInMarketing Context triple: [La Ville Rose, usedInMarketing, promotion of Toulouse as a tourist destination]
-
A.
usedInBrand
Indicates that something (such as a component, material, or element) is utilized as part of or within a particular brand.
-
B.
usedInECommerce
Indicates that something is employed or applied within the context of electronic commerce activities or systems.
-
C.
areUsedIn
chosen
Indicates that certain entities serve as components, tools, or resources within a particular process, context, or application.
-
D.
usedOn
Indicates that one entity is applied to, operated on, or otherwise utilized in relation to another entity.
-
E.
usedInDigitalMedia
Indicates that something is employed, featured, or incorporated within digital media content or platforms.
- 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_69bd43ee52048190b81a4f066534ffb3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6466354481908595f5bb56025cdb |
completed | March 20, 2026, 3:14 p.m. |
| PD | Predicate disambiguation | batch_69bd6221c3b881908604f35f8de6f16b |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:19 p.m.