Triple
T18192394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gerardo Roxas |
E435570
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gerardo |
E328746
|
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: Gerardo | Statement: [Gerardo Roxas, givenName, Gerardo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gerardo Context triple: [Gerardo Roxas, givenName, Gerardo]
-
A.
Gerardo
chosen
Gerardo is a masculine given name of Germanic origin, commonly used in Spanish and Italian-speaking countries.
-
B.
Raúl
Raúl is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
C.
Gustavo
Gustavo is a masculine given name commonly used in Spanish- and Portuguese-speaking countries, equivalent to the name Gustaf.
-
D.
Jorge
Jorge is a masculine given name of Spanish and Portuguese origin, equivalent to George in English.
-
E.
Jorge
Jorge is a character portrayed by actor Giancarlo Esposito, known for his nuanced and often intense roles in film and television.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e0d05974819094b4a50d081be881 |
completed | April 19, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03e22c135881909d4c6a1e93c768a5 |
completed | May 13, 2026, 2:30 a.m. |
Created at: April 10, 2026, 10:31 a.m.