Triple
T1737791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Synagogue of Lutsk |
E37958
|
entity |
| Predicate | wallThickness |
P9690
|
FINISHED |
| Object | unusually thick for a synagogue |
—
|
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: unusually thick for a synagogue | Statement: [Great Synagogue of Lutsk, wallThickness, unusually thick for a synagogue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wallThickness Context triple: [Great Synagogue of Lutsk, wallThickness, unusually thick for a synagogue]
-
A.
wallMaterial
Indicates that one entity is the material from which a wall or walls of another entity are constructed.
-
B.
thickness
chosen
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
C.
deckArmorThickness
Indicates the thickness of the armor plating on the horizontal deck surface of a vehicle, vessel, or structure.
-
D.
hasWallShape
Indicates that an entity possesses a wall whose form or outline matches a specified geometric or structural shape.
-
E.
armorTurretFaceThickness
Indicates the thickness of the armor on the front-facing surface of a turret.
- 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_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab3c2559ac8190905186406fcaccb9 |
completed | March 6, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69aa61c4023c819099cbe439aefda71f |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.