Triple
T9086919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dankovsky Uyezd |
E217780
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Dankov |
E777351
|
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: Dankov | Statement: [Dankovsky Uyezd, namedAfter, Dankov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dankov Context triple: [Dankovsky Uyezd, namedAfter, Dankov]
-
A.
Dankov
chosen
Dankov is a historic town in Russia that once served as an administrative center in the former Dankovsky Uyezd.
-
B.
Kuzminki
Kuzminki is a Moscow Metro station on the Tagansko–Krasnopresnenskaya Line serving the Kuzminki District in southeastern Moscow.
-
C.
Solkan
Solkan is a settlement in western Slovenia, known for its historic stone railway bridge over the Soča River and its proximity to the town of Nova Gorica.
-
D.
Dolgan
Dolgan is a Turkic language spoken primarily by the Dolgan people in northern Siberia, especially in Russia’s Taymyr Peninsula.
-
E.
Kopaska
Kopaska is the Indonesian Navy’s elite frogman and special operations unit, specializing in underwater demolition, maritime sabotage, and counter-terrorism missions.
- 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_69ca83d8ab5881909d8fddae363b32b1 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc9654cb3c819089fa8c0ab0841c81 |
completed | April 1, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0478fc0c4819090de2b761804166f |
completed | April 3, 2026, 11:04 p.m. |
Created at: March 30, 2026, 7:13 p.m.