Triple
T14932407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mykola Azarov |
E372300
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Kaluga |
E314023
|
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: Kaluga | Statement: [Mykola Azarov, placeOfBirth, Kaluga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaluga Context triple: [Mykola Azarov, placeOfBirth, Kaluga]
-
A.
Kaluga
chosen
Kaluga is a historic city in western Russia known as a regional administrative center and an important site in several Russian uprisings and military campaigns.
-
B.
Kirov
Kirov is the revolutionary pseudonym of Sergei Kirov, a prominent early Soviet political leader and close associate of Joseph Stalin.
-
C.
Kirov
Kirov is a town in Kaluga Oblast, Russia, known as a local administrative and industrial center.
-
D.
Kirov
Kirov is a Soviet nuclear-powered guided-missile battlecruiser that served as the lead ship of one of the largest and most heavily armed surface combatant classes built since World War II.
-
E.
Sverdlovsk
Sverdlovsk is the former name of Yekaterinburg, a major industrial and cultural city in Russia’s Ural region.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded646a0808190ba5c0c91bde011c5 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0035428e608190b8bb41dabda044d1 |
completed | May 10, 2026, 7:35 a.m. |
Created at: April 10, 2026, 2:37 a.m.