Triple
T39030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William |
E772
|
entity |
| Predicate | cognate |
P2527
|
FINISHED |
| Object | Guillermo |
E11442
|
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: Guillermo | Statement: [William, cognate, Guillermo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guillermo Context triple: [William, cognate, Guillermo]
-
A.
Guillermo
chosen
Guillermo is the Spanish form of the given name William, commonly used in Spanish-speaking countries.
-
B.
Andrés
Andrés is a Spanish given name commonly used as the equivalent of Andrew.
-
C.
Jorge
Jorge is the birth name of Pope Francis, the head of the Roman Catholic Church and the first pope from the Americas.
-
D.
Felipe de Neve
Felipe de Neve was an 18th-century Spanish colonial governor of California best known for establishing the city of Los Angeles.
-
E.
Juan Bohón
Juan Bohón was a Spanish conquistador best known as the founder of the Chilean city of La Serena in the 16th century.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24ec1ef5481909daf99654dfa3f57 |
completed | Feb. 28, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a291749c688190930934f54de48907 |
completed | Feb. 28, 2026, 6:55 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.