Triple
T8945286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Accrington |
E213205
|
entity |
| Predicate | brickTypeUsedFor |
P86475
|
FINISHED |
| Object | major construction projects in the UK |
—
|
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: major construction projects in the UK | Statement: [Accrington, brickTypeUsedFor, major construction projects in the UK]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brickTypeUsedFor Context triple: [Accrington, brickTypeUsedFor, major construction projects in the UK]
-
A.
cementType
Indicates the specific kind or classification of cement associated with an entity.
-
B.
streetMaterial
Indicates the material composition from which a street or road surface is made.
-
C.
numberOfBricksUsed
Indicates the quantity of bricks that were utilized in constructing or forming something.
-
D.
wallMaterial
Indicates that one entity is the material from which a wall or walls of another entity are constructed.
-
E.
brickProcessorType
Indicates the specific kind or category of processor associated with a given brick.
- 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_69ca839843408190a39069a029a89f15 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66db998c8190999a7a686bbdda1f |
completed | April 1, 2026, 12:29 a.m. |
| PD | Predicate disambiguation | batch_69cc5ed5267c8190a43feb2a2f3df1ec |
completed | March 31, 2026, 11:55 p.m. |
| PDg | Predicate description generation | batch_69cc60e0d7208190966797ce5f95fe49 |
completed | April 1, 2026, 12:03 a.m. |
Created at: March 30, 2026, 6:59 p.m.