Triple
T11521489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andrei Platonov |
E273172
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Andrei |
E2779
|
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: Andrei | Statement: [Andrei Platonov, givenName, Andrei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andrei Context triple: [Andrei Platonov, givenName, Andrei]
-
A.
Andrei
chosen
Andrei is a masculine given name commonly used in Slavic and Eastern European countries, equivalent to the English name Andrew.
-
B.
Yevgeny
Yevgeny is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
C.
Sergei
Sergei is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
D.
Rodion
Rodion is a masculine given name of Slavic origin, most notably borne by Soviet military commander Rodion Malinovsky.
-
E.
Anatoly
Anatoly is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d87fd129c88190893b95222480e04a |
completed | April 10, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f470faab4c8190ac9091713080dc7a |
completed | May 1, 2026, 9:23 a.m. |
Created at: April 8, 2026, 9:36 p.m.