Triple
T5851651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ingolstadt |
E130045
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | Moscow-Zelenograd |
E392816
|
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: Moscow-Zelenograd | Statement: [Ingolstadt, twinCity, Moscow-Zelenograd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moscow-Zelenograd Context triple: [Ingolstadt, twinCity, Moscow-Zelenograd]
-
A.
Zelenograd
chosen
Zelenograd is a district of Moscow, Russia, known as a major center for electronics, microelectronics, and high-tech industry, often referred to as Russia’s "Silicon Valley."
-
B.
Zelenogradsk
Zelenogradsk is a coastal resort town in Russia’s Kaliningrad Oblast on the Baltic Sea, known for its beaches, historic architecture, and proximity to the Curonian Spit.
-
C.
Elektrostal
Elektrostal is an industrial city in Russia known for its metallurgical and engineering industries, located east of Moscow.
-
D.
Serpukhov
Serpukhov is a historic Russian town south of Moscow known for its medieval monasteries, industrial heritage, and location on the Nara River.
-
E.
Dolgoprudny
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.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c035176c8c81909e24d263e4feb664 |
completed | March 22, 2026, 6:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c14127afdc8190a3deb020133ad894 |
completed | March 23, 2026, 1:33 p.m. |
Created at: March 22, 2026, 3:55 p.m.