Triple
T20151998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anton Arensky |
E491454
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Anton |
—
|
NE NERFINISHED |
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: Anton | Statement: [Anton Arensky, givenName, Anton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anton Context triple: [Anton Arensky, givenName, Anton]
-
A.
Anton
chosen
Anton is a masculine given name of Latin origin, commonly used in various European and Slavic countries.
-
B.
Anton
Anton is a film and television production company known for producing genre-driven and elevated horror projects.
-
C.
Andrei
Andrei is a masculine given name commonly used in Slavic and Eastern European countries, equivalent to the English name Andrew.
-
D.
Anatole
Anatole is the famously temperamental and gifted French chef employed by Aunt Dahlia in P. G. Wodehouse’s Jeeves and Wooster stories.
-
E.
Anatole
Anatole is a charming but morally dubious Russian aristocrat from Leo Tolstoy’s novel "War and Peace."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667dc34e081908e42e4c1bde26170 |
completed | April 20, 2026, 5:52 p.m. |
Created at: April 11, 2026, 11:33 p.m.