Triple
T1568697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014 Winter Olympics |
E33489
|
entity |
| Predicate | torchRelayStartLocation |
P8094
|
FINISHED |
| Object | 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: Moscow | Statement: [2014 Winter Olympics, torchRelayStartLocation, Moscow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moscow Context triple: [2014 Winter Olympics, torchRelayStartLocation, Moscow]
-
A.
Moscow
chosen
Moscow is the capital and largest city of Russia, serving as its political, economic, and cultural center.
-
B.
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.
-
C.
Pushkino
Pushkino is a town in Russia that serves as a suburban residential and industrial center northeast of Moscow.
-
D.
Elektrostal
Elektrostal is an industrial city in Russia known for its metallurgical and engineering industries, located east of Moscow.
-
E.
Sofya
Sofya is the Russian given name of Sophia Tolstaya, the wife and muse of novelist Leo Tolstoy.
- 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_69a885f11b048190935025a035302715 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a908a15e308190b8bec55d1712812a |
completed | March 5, 2026, 4:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad401b98888190ad48fbfb6e075434 |
completed | March 8, 2026, 9:23 a.m. |
Created at: March 4, 2026, 7:27 p.m.