Triple
T9819164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuntsevskaya |
E238482
|
entity |
| Predicate | owner |
P347
|
FINISHED |
| Object | Moscow city |
E815804
|
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 city | Statement: [Kuntsevskaya, owner, Moscow city]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moscow city Context triple: [Kuntsevskaya, owner, Moscow city]
-
A.
Moscow City
chosen
Moscow City is a modern high-rise business district in western Moscow known for its cluster of skyscrapers, financial institutions, and commercial developments.
-
B.
Moscow
Moscow is the capital and largest city of Russia, serving as its political, economic, and cultural center.
-
C.
Moscow
Moscow is a fictional character from the Spanish television series "Money Heist" (La Casa de Papel), known as a kind-hearted, blue-collar miner and the father of Denver who participates in the Royal Mint heist.
-
D.
Mosca
Mosca is the cunning and manipulative servant in Ben Jonson’s play "Volpone," known for orchestrating deceptions and driving much of the plot’s dark comedy.
-
E.
Saint Petersburg Federal City
Saint Petersburg Federal City is a major Russian federal subject centered on the historic city of Saint Petersburg, a key cultural, scientific, and industrial hub in northwestern 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_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb2f74e348190be8e4394ae6fe3fe |
completed | April 2, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eacbc2348190bac1cc7f41a389b9 |
completed | April 5, 2026, 4:53 a.m. |
Created at: March 30, 2026, 8:31 p.m.