Triple
T30834812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Parc Bordeaux housing transformation |
E785330
|
entity |
| Predicate | numberOfDwellingsTransformed |
P19276
|
FINISHED |
| Object | approximately 530 |
—
|
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: approximately 530 | Statement: [Grand Parc Bordeaux housing transformation, numberOfDwellingsTransformed, approximately 530]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDwellingsTransformed Context triple: [Grand Parc Bordeaux housing transformation, numberOfDwellingsTransformed, approximately 530]
-
A.
numberOfHousingUnits
chosen
Indicates the total count of distinct housing units associated with an entity or within a specified area.
-
B.
numberOfHouses
Indicates the quantity of houses associated with a given entity or context.
-
C.
hasNumberOfTownhouses
Indicates the specific count of townhouses associated with or contained by a given entity.
-
D.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
-
E.
buildingConvertedTo
Indicates that one building has been transformed, repurposed, or adapted into another type or use of building.
- 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_69f224b73d8c81908129383bfb397c87 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a00a22e32f481909b6006c5b1cdefd3 |
completed | May 10, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_6a00a1bd9c908190b4aa17a61f48126f |
completed | May 10, 2026, 3:18 p.m. |
Created at: April 29, 2026, 8:45 p.m.