Triple
T26410743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château de Coucy |
E663951
|
entity |
| Predicate | wallThicknessOfKeep |
P160527
|
FINISHED |
| Object | approximately 7 meters |
—
|
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 7 meters | Statement: [Château de Coucy, wallThicknessOfKeep, approximately 7 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wallThicknessOfKeep Context triple: [Château de Coucy, wallThicknessOfKeep, approximately 7 meters]
-
A.
wallThicknessComparedTo
Indicates how the thickness of one wall relates to the thickness of another wall, typically in terms of being greater, equal, or less.
-
B.
hasCityWallThickness
Indicates that an entity (such as a city or fortification) is associated with a specific measurement of the thickness of its defensive walls.
-
C.
enclosureWallHeight
Indicates the height of the wall that forms the boundary or enclosure around an area or structure.
-
D.
wallMaterial
Indicates that one entity is the material from which a wall or walls of another entity are constructed.
-
E.
domeThickness
Indicates the measured or specified thickness of a dome structure in the relationship.
- 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_69ee883931888190901be96d75ee23cc |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f6113143f481909c64dfc1975e3a59 |
completed | May 2, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f6018ceb1c8190a6a5f84071659a96 |
completed | May 2, 2026, 1:52 p.m. |
Created at: April 26, 2026, 11:37 p.m.