Triple
T11694071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferike Boros |
E277945
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ferike Boros |
E277945
|
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: Ferike Boros | Statement: [Ferike Boros, name, Ferike Boros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ferike Boros Context triple: [Ferike Boros, name, Ferike Boros]
-
A.
Ferike Boros
chosen
Ferike Boros was a Hungarian-American character actress known for her supporting roles in Hollywood films of the 1930s and 1940s.
-
B.
Bruno Pésery
Bruno Pésery is a French film producer known for his work on notable art-house and auteur-driven films.
-
C.
Roger Borsa
Roger Borsa was an 11th-century Norman duke who ruled Apulia and Calabria in southern Italy, succeeding his father Robert Guiscard.
-
D.
Ferenc Dávid
Ferenc Dávid was a 16th-century Transylvanian religious reformer and theologian who became a leading figure of early Unitarianism and a pioneer of religious tolerance in Europe.
-
E.
Daniel Marhely
Daniel Marhely is a French tech entrepreneur best known for co-founding the music streaming service Deezer.
- 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_69d6aafe02d881909900d54ad7d4af84 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a47b9eb48190976a35e91e25b56b |
completed | April 10, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef1461b2f0819091ef2a0627ffe5f5 |
completed | April 27, 2026, 7:46 a.m. |
Created at: April 8, 2026, 9:40 p.m.