Triple
T6244987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diego Laínez |
E139697
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Diego |
E15778
|
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: Diego | Statement: [Diego Laínez, givenName, Diego]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diego Context triple: [Diego Laínez, givenName, Diego]
-
A.
Diego
chosen
Diego is a given name of Spanish origin commonly used in Spanish-speaking countries and beyond.
-
B.
Raymundo
Raymundo is a masculine given name, commonly used in Spanish- and Portuguese-speaking cultures, that is related to the name Ramón.
-
C.
Sebastián
Sebastián is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
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_69c008b1c5088190ae6de2555fc05ad8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0631c63d48190a41ec1232aecb373 |
completed | March 22, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64ba0968c819091fae910b8c82879 |
completed | March 27, 2026, 9:19 a.m. |
Created at: March 22, 2026, 4:23 p.m.