Triple
T14580727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valter Skarsgård |
E342184
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Valter |
E174146
|
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: Valter | Statement: [Valter Skarsgård, givenName, Valter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valter Context triple: [Valter Skarsgård, givenName, Valter]
-
A.
Valter
chosen
Valter is a masculine given name used in various European countries, generally equivalent to the English name Walter.
-
B.
Anton Valter
Anton Valter was a physicist associated with the Kharkiv Institute of Physics and Technology, known for his contributions to Soviet-era scientific research.
-
C.
Valentim
Valentim is a given name, primarily used in Portuguese-speaking countries, that corresponds to the variant of the name Valentine.
-
D.
Veit
Veit is a German surname most notably associated with the 19th-century Romantic painter Philipp Veit.
-
E.
Jost Vacano
Jost Vacano is a German cinematographer renowned for his dynamic, technically innovative work on films such as "Das Boot," "RoboCop," and other major international productions.
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb3f6f78c81908a30ecb4c025299d |
completed | April 14, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94b832b08190965b727baa700403 |
completed | May 8, 2026, 7:46 a.m. |
Created at: April 10, 2026, 1:24 a.m.