Triple
T6576469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eduardo Ciannelli |
E157175
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Eduardo |
E37162
|
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: Eduardo | Statement: [Eduardo Ciannelli, givenName, Eduardo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eduardo Context triple: [Eduardo Ciannelli, givenName, Eduardo]
-
A.
Eduardo
chosen
Eduardo is a masculine given name commonly used in Spanish and Portuguese-speaking countries, equivalent to the English name Edward.
-
B.
Enrique
Enrique is a Spanish given name equivalent to the English name Henry.
-
C.
Eugenio
Eugenio is a masculine given name of Greek origin, commonly used in Spanish- and Italian-speaking countries.
-
D.
Ernesto
Ernesto is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
E.
Vicente
Vicente is a given name, common in Spanish- and Portuguese-speaking countries, that corresponds to the English name Vincent.
- 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_69c6882b3a108190b3a9eb343ae4162c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae7399488190b5f6948c60188aec |
completed | March 27, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7127fc0a081909589c2a05e457866 |
completed | March 27, 2026, 11:27 p.m. |
Created at: March 27, 2026, 1:54 p.m.