Triple
T9444594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunta Stölzl |
E227731
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gunta |
E227730
|
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: Gunta | Statement: [Gunta Stölzl, givenName, Gunta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gunta Context triple: [Gunta Stölzl, givenName, Gunta]
-
A.
Gunta
chosen
Gunta is a given name most notably borne by Gunta Stölzl, a pioneering textile artist and the only female master at the Bauhaus school.
-
B.
Guisa
Guisa is a municipality and town located in Cuba’s eastern Granma Province, known for its rural character and historical significance in the Cuban Revolution.
-
C.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
D.
Günther
Günther is the zoologist who first formally described the impressed tortoise species Manouria impressa.
-
E.
Gugino
Gugino is the surname of American actress Carla Gugino, known for her versatile roles in film and television.
- 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_69ca843884488190ad6cbe0153088234 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f32aee88190a43573f97fa1e49d |
completed | April 1, 2026, 8:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d110669ca48190bbaf772e2e6aa457 |
completed | April 4, 2026, 1:21 p.m. |
Created at: March 30, 2026, 7:51 p.m.