Triple
T5684451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alberto Cavos |
E125276
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Alberto |
E65543
|
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: Alberto | Statement: [Alberto Cavos, givenName, Alberto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alberto Context triple: [Alberto Cavos, givenName, Alberto]
-
A.
Alberto
chosen
Alberto is a masculine given name common in Romance-language countries, derived from the Germanic name Albert and sharing its meaning of "noble" or "bright."
-
B.
Roberto
Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
-
C.
Hércules Barsotti
Hércules Barsotti was a Brazilian artist known for his contributions to geometric abstraction and concrete art in the mid-20th century.
-
D.
Adolfo
Adolfo is a masculine given name, commonly used in Spanish and Italian, that derives from the Germanic name Adolf.
-
E.
Alberto Verso
Alberto Verso is an Italian costume designer known for his work on films such as "Tea with Mussolini."
- 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_69c0082a884c8190a79001bae658941f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c023b8efc4819085675d0d3dfb2a54 |
completed | March 22, 2026, 5:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b0a7aff08190bca93ac0ab8a9be0 |
completed | March 23, 2026, 3:16 a.m. |
Created at: March 22, 2026, 3:44 p.m.