Triple
T1401324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Town (Vieux-Nice) |
E30788
|
entity |
| Predicate | typicalBuildingHeight |
P573
|
FINISHED |
| Object | 4–5 stories |
—
|
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: 4–5 stories | Statement: [Old Town (Vieux-Nice), typicalBuildingHeight, 4–5 stories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBuildingHeight Context triple: [Old Town (Vieux-Nice), typicalBuildingHeight, 4–5 stories]
-
A.
hasBuildingHeightType
Indicates the classification or type used to characterize the height of a building in the relationship.
-
B.
typicalHeight
chosen
Indicates the usual or characteristic height associated with an entity, such as a person, object, or species.
-
C.
roofHeight
Indicates the vertical distance or elevation of a roof relative to a reference level or structure.
-
D.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
-
E.
towerType
Indicates the specific kind or classification of a tower that an entity is associated with or represents.
- 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_69a498fd4e408190bd73eca30ea9754c |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c39da20c8190b9c82cc4ad747c66 |
completed | March 1, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69a4bf017f8081908572121560ec621f |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.