Triple
T13515938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweetie |
E322759
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Veronika Jenet |
E868308
|
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: Veronika Jenet | Statement: [Sweetie, editedBy, Veronika Jenet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Veronika Jenet Context triple: [Sweetie, editedBy, Veronika Jenet]
-
A.
Veronika Jenet
chosen
Veronika Jenet is an Australian film editor best known for her acclaimed work on feature films such as "The Piano."
-
B.
Zita Horváth
Zita Horváth is a Hungarian academic and university leader who serves as the rector of the University of Miskolc.
-
C.
Klara Pölzl
Klara Pölzl was the mother of Adolf Hitler, remembered primarily for her role in his early life and family background in late 19th-century Austria.
-
D.
Ágnes Hranitzky
Ágnes Hranitzky is a Hungarian film editor and co-director best known for her long-term creative collaboration with filmmaker Béla Tarr on his distinctive, slow-paced art films.
-
E.
Zora Vesecká
Zora Vesecká is a Czech individual whose given name is Zora, a common female name in Slavic 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_69d80766a21881909f21a1b7421d3b8a |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafa0ed508190b2855171b1945e84 |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7942668f481909c6d892fdfd32c02 |
completed | May 3, 2026, 6:29 p.m. |
Created at: April 9, 2026, 9:44 p.m.