Triple
T16405723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiot |
E398420
|
entity |
| Predicate | hasEtymologicalRoot |
P5801
|
FINISHED |
| Object | Wido |
E146517
|
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: Wido | Statement: [Wiot, hasEtymologicalRoot, Wido]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wido Context triple: [Wiot, hasEtymologicalRoot, Wido]
-
A.
Wido
chosen
Wido is a given name that functions as a variant form of the name Guido, used in various European languages.
-
B.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
C.
Günther
Günther is the zoologist who first formally described the impressed tortoise species Manouria impressa.
-
D.
Benno
Benno is a masculine given name, used as a variant or extended form of the name Ben in various European languages.
-
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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e327d2b4e48190b7153f198639e9cd |
completed | April 18, 2026, 6:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c62614c8190acd6d211cab1be11 |
completed | May 10, 2026, 8:05 a.m. |
Created at: April 10, 2026, 5:09 a.m.