Triple
T8850934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Krasny |
E210634
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Krasny |
E128983
|
NE 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: Krasny | Statement: [Robert Krasny, familyName, Krasny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krasny Context triple: [Robert Krasny, familyName, Krasny]
-
A.
Krasny
Krasny is a town situated along the Sozh River, known for its regional administrative and cultural significance.
-
B.
Kraslava
Kraslava is a small town in southeastern Latvia known for its historic architecture and scenic location near the borders with Belarus and Lithuania.
-
C.
Krasnov
chosen
Krasnov is a Russian surname borne by various notable figures in military, political, and cultural history.
-
D.
Krasnoturyinsk
Krasnoturyinsk is an industrial town in Russia’s Ural region known for its mining and metallurgical industries.
-
E.
Krasnogorsk
Krasnogorsk is a city in western Russia that serves as an important administrative and residential center just outside Moscow.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca838a424c8190b1ecac115c2927e7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60c2300c819097b1ca6ebe2f749a |
completed | April 1, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfab821e808190a918bf787cde54b6 |
completed | April 3, 2026, 11:58 a.m. |
Created at: March 30, 2026, 6:49 p.m.