Triple
T35971091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Изборская крепость |
E1040285
|
entity |
| Predicate | толщинаСтен |
P136195
|
FINISHED |
| Object | до 3 метров |
—
|
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: до 3 метров | Statement: [Изборская крепость, толщинаСтен, до 3 метров]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: толщинаСтен Context triple: [Изборская крепость, толщинаСтен, до 3 метров]
-
A.
wallThicknessComparedTo
Indicates how the thickness of one wall relates to the thickness of another wall, typically in terms of being greater, equal, or less.
-
B.
roofThickness
Indicates the measured or specified thickness of a roof in the relationship.
-
C.
hasCityWallThickness
chosen
Indicates that an entity (such as a city or fortification) is associated with a specific measurement of the thickness of its defensive walls.
-
D.
domeThickness
Indicates the measured or specified thickness of a dome structure in the relationship.
-
E.
wallThicknessOfKeep
Indicates the thickness of the walls of a keep in a fortification or castle.
- 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_69f76e27758c81909b711cf38a130aaf |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7acaec1508190a38f2ac9cc5383e7 |
completed | May 3, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69f7ab75387c819091afc3c2128eb903 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 4:07 p.m.