Triple
T14796375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arsenio Martínez Campos |
E347788
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Arsenio |
E83128
|
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: Arsenio | Statement: [Arsenio Martínez Campos, givenName, Arsenio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arsenio Context triple: [Arsenio Martínez Campos, givenName, Arsenio]
-
A.
Arsenio
chosen
Arsenio is a masculine given name of Spanish origin, historically borne by several notable figures in Spanish-speaking countries.
-
B.
Eugenio
Eugenio is a masculine given name of Greek origin, commonly used in Spanish- and Italian-speaking countries.
-
C.
Ernesto
Ernesto is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
D.
Arturo
Arturo is a masculine given name of Spanish and Italian origin, equivalent to Arthur in English.
-
E.
Elicio
Elicio is a shepherd and one of the principal pastoral protagonists in Miguel de Cervantes’ early novel "La Galatea."
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decd5fdd548190a2ee5e668c2b20b4 |
completed | April 14, 2026, 11:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72a4f71881909c3c1cc09fe89a60 |
completed | May 8, 2026, 11:32 p.m. |
Created at: April 10, 2026, 1:31 a.m.