Triple
T12246885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugen Bleuler |
E291873
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Eugen |
E305910
|
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: Eugen | Statement: [Eugen Bleuler, givenName, Eugen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eugen Context triple: [Eugen Bleuler, givenName, Eugen]
-
A.
Eugen
Eugen is the given first name of the influential German playwright and poet Bertolt Brecht.
-
B.
Eugen
chosen
Eugen is a masculine given name of Greek origin, commonly used in various European languages and derived from a word meaning "well-born" or "noble."
-
C.
Günther
Günther is the zoologist who first formally described the impressed tortoise species Manouria impressa.
-
D.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
E.
Woldemar
Woldemar is a masculine given name of Germanic origin, commonly considered a variant of Waldemar.
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cb893a08190bbdfcb23082b8f34 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60ab9a9b08190903c1ce6d91af2b5 |
completed | May 2, 2026, 2:31 p.m. |
Created at: April 8, 2026, 9:51 p.m.