Triple
T37692334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tempelgasse synagogue memorial site |
E938836
|
entity |
| Predicate | marksDestructionOf |
P189188
|
FINISHED |
| Object | Tempelgasse synagogue |
—
|
NE NERFINISHED |
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: Tempelgasse synagogue | Statement: [Tempelgasse synagogue memorial site, marksDestructionOf, Tempelgasse synagogue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marksDestructionOf Context triple: [Tempelgasse synagogue memorial site, marksDestructionOf, Tempelgasse synagogue]
-
A.
destroyedFor
Indicates that one entity was ruined, eliminated, or rendered unusable specifically for the benefit, purpose, or objective of another entity.
-
B.
destroyedDuring
Indicates that one entity was destroyed in the course of, or as a consequence of, a specified event or time period.
-
C.
destroyedAfter
Indicates that one entity is destroyed at a point in time that occurs after the destruction of another entity.
-
D.
extinguishingMarks
Indicates that one entity causes another entity to cease burning, glowing, or otherwise producing light or flame.
-
E.
destroyedInText
Indicates that one entity is described as being destroyed within the content or narrative of a given text.
- F. None of above. chosen
Provenance (4 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_69f76eda6ae48190b3111071eeacc038 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbb9e8108c8190ae1c7940b1677e95 |
completed | May 6, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fbb141605c8190b9c27d70352522db |
completed | May 6, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69fbb9e69b7481909beaf8264d87c5e5 |
completed | May 6, 2026, 10 p.m. |
Created at: May 3, 2026, 4:18 p.m.