Triple
T1823454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugène |
E40593
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Eugen |
E187271
|
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: [Eugène, relatedName, Eugen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eugen Context triple: [Eugène, relatedName, Eugen]
-
A.
Eugen
chosen
Eugen is the given first name of the influential German playwright and poet Bertolt Brecht.
-
B.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
C.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
-
D.
Theodor
Theodor is the given name of Emil Theodor Kocher, a Swiss surgeon and Nobel laureate renowned for his pioneering work in thyroid surgery.
-
E.
Erwin
Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
- 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_69a8864526c081908a3a4d74f689e2c5 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa662d13e88190a22b0faf0d848c7d |
completed | March 6, 2026, 5:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adead6888081909f89704f0c070d68 |
completed | March 8, 2026, 9:32 p.m. |
Created at: March 4, 2026, 7:32 p.m.