Triple
T15501710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kunstfort Vijfhuizen |
E378972
|
entity |
| Predicate | usesBuildingPreviouslyUsedFor |
P1267
|
FINISHED |
| Object | military defense |
—
|
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: military defense | Statement: [Kunstfort Vijfhuizen, usesBuildingPreviouslyUsedFor, military defense]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesBuildingPreviouslyUsedFor Context triple: [Kunstfort Vijfhuizen, usesBuildingPreviouslyUsedFor, military defense]
-
A.
usedOnBuildingOf
Indicates that something is applied to, installed on, or otherwise utilized on the structure of a particular building.
-
B.
usesBuilding
chosen
Indicates that one entity makes use of, occupies, or operates within a particular building.
-
C.
previousBuildingUse
Indicates that a building previously served a specified use or function before its current one.
-
D.
constructionUsed
Indicates that one entity was employed as a construction method, material, or component in creating or assembling another entity.
-
E.
hasFormerBuilding
Indicates that an entity previously occupied or used a different building, which is identified as its former 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcc5bb88190b8a9a81419a9a38b |
completed | April 16, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69ded2896a9c8190a8b9627deb3c17b4 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:54 a.m.