Triple
T11221898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taganskaya |
E265589
|
entity |
| Predicate | ownedBy |
P347
|
FINISHED |
| Object | City of Moscow |
E1747
|
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: City of Moscow | Statement: [Taganskaya, ownedBy, City of Moscow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Moscow Context triple: [Taganskaya, ownedBy, City of Moscow]
-
A.
Moscow City
Moscow City is a modern high-rise business district in western Moscow known for its cluster of skyscrapers, financial institutions, and commercial developments.
-
B.
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.
-
C.
Pushkino
Pushkino is a town in Russia that serves as a suburban residential and industrial center northeast of Moscow.
-
D.
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.
-
E.
Moscow
chosen
Moscow is the capital and largest city of Russia, serving as its political, economic, and cultural center.
- 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8ec8fb08190b27144ab65f85957 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4977cab4481909c6b94ca07cd5e4a |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 8, 2026, 9:30 p.m.