Triple
T14575485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santiago |
E342032
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Yago |
E703075
|
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: Yago | Statement: [Santiago, hasVariant, Yago]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yago Context triple: [Santiago, hasVariant, Yago]
-
A.
Yago
chosen
Yago is a given name, often used as a variant of Santiago in Spanish- and Portuguese-speaking contexts.
-
B.
Norberto
Norberto is an Italian given name most notably borne by the influential legal and political philosopher Norberto Bobbio.
-
C.
Marcelo
Marcelo is a common Portuguese and Spanish given name, notably borne by figures such as Brazilian footballer Marcelo Vieira and former Portuguese Prime Minister Marcelo Caetano.
-
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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb3f49d58819094fcd2a702e146cb |
completed | April 14, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8acc788081909c41905785fa9a29 |
completed | May 8, 2026, 7:03 a.m. |
Created at: April 10, 2026, 1:24 a.m.