Triple
T9335570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Law Courts of Brussels |
E224633
|
entity |
| Predicate | hasRestorationWork |
P13045
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Law Courts of Brussels, hasRestorationWork, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRestorationWork Context triple: [Law Courts of Brussels, hasRestorationWork, yes]
-
A.
hasReconstructionWork
Indicates that an entity is undergoing, has undergone, or is associated with reconstruction or restoration work.
-
B.
haveReconstructionWork
Indicates that an entity is undergoing or is associated with reconstruction or restoration work.
-
C.
hasRestoration
chosen
Indicates that an entity has undergone, is undergoing, or is associated with a process of repair, renewal, or restoration.
-
D.
hasRestorationActivities
Indicates that an entity carries out, is involved in, or is associated with actions aimed at restoring or rehabilitating another entity or resource.
-
E.
requiresRenovation
Indicates that an entity is in a condition that necessitates repair, updating, or refurbishment before it is suitable for normal use or standards.
- 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_69ca84286fcc81909f6e7fd7a7e862a2 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd37f031888190a90d263d225d163c |
completed | April 1, 2026, 3:21 p.m. |
| PD | Predicate disambiguation | batch_69cc7a66aef08190b8d668cff5b04f5f |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:40 p.m.