Triple
T12245088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Jewish Cemetery in Prague |
E291829
|
entity |
| Predicate | numberOfGravestones |
P41332
|
FINISHED |
| Object | approximately 12000 |
—
|
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: approximately 12000 | Statement: [Old Jewish Cemetery in Prague, numberOfGravestones, approximately 12000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGravestones Context triple: [Old Jewish Cemetery in Prague, numberOfGravestones, approximately 12000]
-
A.
numberOfHeadstones
chosen
Indicates the total count of headstones associated with a given entity or location.
-
B.
numberOfCemeteries
Indicates the count of cemeteries associated with a given entity or within a specified area.
-
C.
numberOfBurials
Indicates the total count of burial events associated with a given entity.
-
D.
numberOfInterred
Indicates the total count of individuals who are buried or interred at a given site or within a specified context.
-
E.
numberOfCoffins
Indicates the quantity of coffins associated with a given entity or situation.
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91d38ee10819093ed41d2954bf4ef |
completed | April 10, 2026, 3:54 p.m. |
| PD | Predicate disambiguation | batch_69d91c46dcd88190a263db30804bff36 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.