Triple
T14981931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vyazemsky District |
E373597
|
entity |
| Predicate | hasUrbanCenter |
P2106
|
FINISHED |
| Object | Vyazemsky |
E350563
|
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: Vyazemsky | Statement: [Vyazemsky District, hasUrbanCenter, Vyazemsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vyazemsky Context triple: [Vyazemsky District, hasUrbanCenter, Vyazemsky]
-
A.
Vyazemsky
chosen
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
B.
Yuryatin
Yuryatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Lara Antipova’s story.
-
C.
Khovrino
Khovrino is a Moscow Metro station serving as the northern terminus of the Zamoskvoretskaya Line.
-
D.
Kamenskiy
Kamenskiy is a Slavic surname, commonly transliterated from Russian or related languages, borne by various individuals across Eastern Europe and the former Soviet Union.
-
E.
Sampsonievsky
Sampsonievsky is a municipal settlement located within the Vyborgsky District of Saint Petersburg, Russia.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6fe42a081909308f788fdf024d5 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea5aaeda08190846c15562e67c1fb |
completed | May 9, 2026, 3:10 a.m. |
Created at: April 10, 2026, 2:52 a.m.