Triple
T13764683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prokshino |
E330707
|
entity |
| Predicate | adjacentStationOnLine |
P41425
|
FINISHED |
| Object | Filatov Lug |
E252548
|
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: Filatov Lug | Statement: [Prokshino, adjacentStationOnLine, Filatov Lug]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Filatov Lug Context triple: [Prokshino, adjacentStationOnLine, Filatov Lug]
-
A.
Lopatina
Lopatina is the feminine form of the Russian surname Lopatin.
-
B.
Paletskaya
Paletskaya is a Russian surname associated with the noblewoman Anna Fyodorovna Paletskaya.
-
C.
Yuryatin
Yuryatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Lara Antipova’s story.
-
D.
Yukhnov
Yukhnov is a small historic town in western Russia known for its location on the Ugra River and its role in regional trade and World War II history.
-
E.
Paveletskaya
chosen
Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de022690ac8190bd5410ecc659a2a7 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a862e6808190b8fbb27304212058 |
completed | May 3, 2026, 7:56 p.m. |
Created at: April 9, 2026, 10:10 p.m.