Triple
T20521761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Stuart |
E503825
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Wolf Lieser |
—
|
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: Wolf Lieser | Statement: [Mary Stuart, spouse, Wolf Lieser]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wolf Lieser Context triple: [Mary Stuart, spouse, Wolf Lieser]
-
A.
Wolf Lieser
chosen
Wolf Lieser is a German gallerist and curator best known for founding the Digital Art Museum (DAM) and promoting digital and computer-based art.
-
B.
Wolf Kretzschmar
Wolf Kretzschmar is a film and television producer known for his work on the Norwegian Netflix series "Home for Christmas."
-
C.
Wolf Kroeger
Wolf Kroeger is a German-born production designer and art director known for his work on major films including the 1980 adaptation of "Popeye."
-
D.
Wolfdietrich Schnurre
Wolfdietrich Schnurre was a German writer and key postwar literary figure known for his short stories, fables, and involvement in the early Federal Republic’s literary renewal.
-
E.
Wolf Leslau
Wolf Leslau was a prominent linguist and scholar of Semitic and Ethiopian languages, renowned for his extensive fieldwork and documentation of endangered tongues.
- 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_69e0b4b3a6e08190ae663701f50fab8e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69f46488c819093687b4e07837793 |
completed | April 20, 2026, 9:48 p.m. |
Created at: April 16, 2026, 11:36 a.m.