Triple
T21978620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marek Janowski |
E542774
|
entity |
| Predicate | specialization |
P466
|
FINISHED |
| Object | Richard Wagner |
—
|
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: Richard Wagner | Statement: [Marek Janowski, specialization, Richard Wagner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Richard Wagner Context triple: [Marek Janowski, specialization, Richard Wagner]
-
A.
Richard Wagner
chosen
Richard Wagner was a 19th-century German composer best known for his revolutionary operas and music dramas, which profoundly shaped Western classical music and artistic thought.
-
B.
Richard Wagner (writer)
Richard Wagner is a Romanian-born German writer and essayist known for his works on life under dictatorship and his association with fellow author Herta Müller.
-
C.
Wagner
Wagner is a surname most famously associated with Honus Wagner, one of the greatest early baseball players in Major League history.
-
D.
Wagner
Wagner is a Federal-Mogul automotive parts brand best known for its replacement brake components and lighting products.
-
E.
Wagner
Wagner is a common Portuguese given name, notably borne by Brazilian actor and filmmaker Wagner Moura.
- 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_69e0c48070988190909db97667b9a0ac |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1248a60708190a9aa8b9b7738c261 |
completed | April 28, 2026, 9:20 p.m. |
Created at: April 16, 2026, 8:03 p.m.