Triple
T203793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Birmingham Museum of Art |
E4564
|
entity |
| Predicate | mainBuildingMaterial |
P618
|
FINISHED |
| Object | concrete |
—
|
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: concrete | Statement: [Birmingham Museum of Art, mainBuildingMaterial, concrete]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainBuildingMaterial Context triple: [Birmingham Museum of Art, mainBuildingMaterial, concrete]
-
A.
materialUsed
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
B.
constructionType
Indicates the specific method or style by which something is built or constructed.
-
C.
buildingType
Indicates the specific category or function that characterizes what kind of building something is.
-
D.
architectureType
Indicates the specific style or category of architecture that characterizes or defines an entity.
-
E.
material
chosen
Indicates that one entity is physically composed of, made from, or constructed using the substance or material represented by the other entity.
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25f46b4f081909e5ee3718109a71f |
completed | Feb. 28, 2026, 3:21 a.m. |
| PD | Predicate disambiguation | batch_69a25b4b42ec8190bef16bbbdd30a742 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.