Triple
T31201763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Theatre (Budapest) |
E795493
|
entity |
| Predicate | hasPredecessorDemolished |
P51835
|
FINISHED |
| Object | 1965 |
—
|
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: 1965 | Statement: [National Theatre (Budapest), hasPredecessorDemolished, 1965]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPredecessorDemolished Context triple: [National Theatre (Budapest), hasPredecessorDemolished, 1965]
-
A.
hasDemolitionOrDestruction
Indicates that one entity causes, undergoes, or is associated with the demolition or destruction of another entity.
-
B.
hasFormerBuilding
Indicates that an entity previously occupied or used a different building, which is identified as its former building.
-
C.
demolishedAfter
Indicates that one entity was demolished at a point in time later than the demolition of another entity.
-
D.
previousBuildingDemolished
chosen
Indicates that a building which previously occupied the same site or fulfilled the same role has been demolished.
-
E.
demolishedOrDestroyed
Indicates that one entity has caused another entity to be torn down, ruined, or rendered unusable, typically through deliberate demolition or destructive force.
- 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_69f224d8c6608190b7882466521f62be |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a004c41f13c8190bc47daf6a5ca3949 |
completed | May 10, 2026, 9:13 a.m. |
| PD | Predicate disambiguation | batch_6a004bce3e3081909b35ae5b3bf2b35e |
completed | May 10, 2026, 9:11 a.m. |
Created at: April 29, 2026, 9:09 p.m.