Triple
T416213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reichstag fire |
E9597
|
entity |
| Predicate | relatedBuilding |
P3158
|
FINISHED |
| Object | German parliament building |
—
|
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: German parliament building | Statement: [Reichstag fire, relatedBuilding, German parliament building]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedBuilding Context triple: [Reichstag fire, relatedBuilding, German parliament building]
-
A.
sharesBuildingWith
Indicates that two entities occupy or use the same building.
-
B.
building
Indicates that one entity constructs, assembles, or develops another entity, typically over a period of time.
-
C.
buildingType
Indicates the specific category or function that characterizes what kind of building something is.
-
D.
relatedPlace
chosen
Indicates a relationship where one place is connected or associated with another place in a relevant or meaningful way.
-
E.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
- 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edcff4688190809d83d112ff25a5 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:09 p.m.