Triple
T12216879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bruno Kreisky |
E291108
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Bruno |
E88243
|
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: Bruno | Statement: [Bruno Kreisky, givenName, Bruno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bruno Context triple: [Bruno Kreisky, givenName, Bruno]
-
A.
Bruno
chosen
Bruno is a masculine given name of Germanic origin, commonly used in various European and Latin American countries.
-
B.
Carlo
Carlo is the Italian form of the given name Charles, commonly used in Italy and other Italian-speaking communities.
-
C.
Sandro
Sandro is a common Italian given name, typically used as a diminutive or short form of Alessandro.
-
D.
Bube
Bube is a Bantu language spoken primarily by the Bubi people on Bioko Island in Equatorial Guinea.
-
E.
Benno
Benno is a masculine given name, used as a variant or extended form of the name Ben in various European languages.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c9419d48190b0037fe8edc681c4 |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60aa31f548190bd4f8cfe3c55614b |
completed | May 2, 2026, 2:30 p.m. |
Created at: April 8, 2026, 9:51 p.m.