Triple
T12459722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerusalem Synagogue in Prague |
E297755
|
entity |
| Predicate | largestIn |
P4495
|
FINISHED |
| Object | Prague (among active synagogues) |
—
|
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: Prague (among active synagogues) | Statement: [Jerusalem Synagogue in Prague, largestIn, Prague (among active synagogues)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: largestIn Context triple: [Jerusalem Synagogue in Prague, largestIn, Prague (among active synagogues)]
-
A.
isLargestOf
chosen
Indicates that one entity has the greatest size, extent, or magnitude among a specified set of entities.
-
B.
largestPartIn
Indicates that one entity is the largest component or segment contained within another entity.
-
C.
hasLargestAreaOf
Indicates that the subject entity possesses the greatest area (size of surface or region) compared to the other entities in the specified set or context.
-
D.
largestInCountry
Indicates that an entity is the largest of its kind within the specified country.
-
E.
largestSpan
Indicates that the referenced entity has the greatest extent or coverage (in distance, time, or range) among a set of comparable spans.
- 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_69d6ada270808190b1a2b2e7b02bb426 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95151e7348190a1d4953a8b416a13 |
completed | April 10, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69d94d3c27a08190a0237200203e476d |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:56 p.m.