Triple
T14983038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rafael Núñez |
E373628
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rafael |
E145536
|
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: Rafael | Statement: [Rafael Núñez, givenName, Rafael]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rafael Context triple: [Rafael Núñez, givenName, Rafael]
-
A.
Rafael
chosen
Rafael is a masculine given name of Hebrew origin, commonly used in Spanish, Portuguese, and other languages, meaning "God has healed."
-
B.
Feliciano
Feliciano is a given name of Latin origin, commonly used in Romance-language countries and related to the name Felix.
-
C.
Rubén
Rubén is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
D.
Roberto
Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
-
E.
Raúl
Raúl is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6fe42a081909308f788fdf024d5 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dc625888190bf98eecf5f5b6707 |
completed | May 9, 2026, 2:36 a.m. |
Created at: April 10, 2026, 2:52 a.m.