Triple
T1721789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rafael Mariano Grossi |
E37408
|
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 Mariano Grossi, givenName, Rafael]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rafael Context triple: [Rafael Mariano Grossi, 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.
Rubén
Rubén is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
C.
Roberto
Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
-
D.
Raúl
Raúl is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
E.
Álvaro
Álvaro is a masculine given name of Spanish origin commonly used in Spain and Latin America.
- 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_69a8861acab88190bb43cde203429399 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa635703dc8190809260de43b72ea3 |
completed | March 6, 2026, 5:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3b9a2f4819082ca2e9f838f7b9e |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:30 p.m.