Triple
T4297690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capitole de Toulouse |
E99754
|
entity |
| Predicate | numberOfColumnsOnFaçade |
P9588
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Capitole de Toulouse, numberOfColumnsOnFaçade, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfColumnsOnFaçade Context triple: [Capitole de Toulouse, numberOfColumnsOnFaçade, 8]
-
A.
numberOfColumnsOnFacade
chosen
Indicates the count of vertical structural or decorative divisions (columns) present on a building’s facade.
-
B.
numberOfColumnsOnFlanks
Indicates the count of columns located on the flanking sides of a structure or object.
-
C.
numberOfFloors
Indicates the total count of distinct floor levels that a building or structure has.
-
D.
façadeDescription
Indicates a textual description that characterizes the appearance, style, or features of a building’s façade.
-
E.
façadeOrientation
Indicates the directional orientation that a building’s façade faces relative to a reference (e.g., cardinal directions or a main street).
- 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_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3509bff808190a86fade7ccfc3611 |
completed | March 12, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69b347fe55a88190b77bab0c0f38e1aa |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:08 p.m.