Triple
T17611762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Widukind |
E428979
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Geva |
—
|
NE NERFINISHED |
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: Geva | Statement: [Widukind, spouse, Geva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Geva Context triple: [Widukind, spouse, Geva]
-
A.
Geva
chosen
Geva is a surname most notably associated with Tamara Geva, a Russian-American actress, dancer, and choreographer.
-
B.
Genberg
Genberg is a Swedish surname most notably associated with individuals such as Hjördis Genberg, a mid-20th-century Swedish model and actress.
-
C.
Lautenthal
Lautenthal is a small historic mining town in Germany’s Harz Mountains, known for its picturesque valley setting and former silver mining industry.
-
D.
Gonda
Gonda is a city in the Indian state of Uttar Pradesh, known for its agricultural economy and proximity to the Ghaghara River.
-
E.
Löwenberg
Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d2dfa688190a0b9b396bb6133cc |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 5:51 a.m.