Triple
T11539993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dolgoprudny |
E273647
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Долгопрудный |
E273647
|
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: Долгопрудный | Statement: [Dolgoprudny, hasNameInLanguage, Долгопрудный]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Долгопрудный Context triple: [Dolgoprudny, hasNameInLanguage, Долгопрудный]
-
A.
Dolgoprudny
chosen
Dolgoprudny is a town in Moscow Oblast, Russia, known for hosting the Moscow Institute of Physics and Technology and forming part of the northern suburbs of Moscow.
-
B.
Lyubertsy
Lyubertsy is a city in Russia that serves as a major suburban and industrial center just southeast of Moscow.
-
C.
Serpukhov
Serpukhov is a historic Russian town south of Moscow known for its medieval monasteries, industrial heritage, and location on the Nara River.
-
D.
Dmitrov
Dmitrov is a historic town in Moscow Oblast, Russia, located north of Moscow and known for its medieval kremlin and role as a regional cultural center.
-
E.
Noginsk
Noginsk is a town in western Russia that serves as an industrial and transport center east of 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d886deed5c81908e5c38156064f882 |
completed | April 10, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e685ab77908190ac5d59cf2b8c96bf |
completed | April 20, 2026, 7:59 p.m. |
Created at: April 8, 2026, 9:37 p.m.