Triple
T11059301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holden Roberto |
E261462
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Roberto |
E26746
|
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: Roberto | Statement: [Holden Roberto, familyName, Roberto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roberto Context triple: [Holden Roberto, familyName, Roberto]
-
A.
Roberto
chosen
Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
-
B.
Fabio
Fabio is the birth name of Pope Alexander VII, the 17th-century head of the Catholic Church and ruler of the Papal States.
-
C.
Sergio
Sergio is a masculine given name commonly used in Spanish and Italian-speaking countries, derived from the Latin name Sergius.
-
D.
Riccardo
Riccardo is an Italian given name, equivalent to Richard in English.
-
E.
Giancarlo
Giancarlo is an Italian masculine given name commonly used in Italy and among Italian communities worldwide.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798a4f3f88190a29710f64cef9d25 |
completed | April 9, 2026, 12:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c87ab0308190a6a6ada1708f0ec2 |
completed | April 18, 2026, 6:07 p.m. |
Created at: April 8, 2026, 9:26 p.m.