Triple
T4103632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kolomna Kremlin |
E88396
|
entity |
| Predicate | numberOfTowersPreserved |
P5319
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Kolomna Kremlin, numberOfTowersPreserved, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTowersPreserved Context triple: [Kolomna Kremlin, numberOfTowersPreserved, 7]
-
A.
numberOfTowers
chosen
Indicates the quantity of towers associated with or contained by a given entity.
-
B.
hasPreservedBuildings
Indicates that an entity possesses buildings that have been maintained or kept in their original or historical condition.
-
C.
hasNumberOfMonuments
Indicates the specific count of monuments associated with or present in a given entity.
-
D.
numberOfDistrictsDestroyed
Indicates the quantity of districts that have been destroyed in a given context or event.
-
E.
buildingsDestroyed
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
- 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_69aed9484fb881909146f4c772ad277c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefd116dac8190952cb2ddf63216ec |
completed | March 9, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69aef90b2ef08190ae84febfd69dd48b |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:40 p.m.