Triple
T8693732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Rosa de Lima |
E206353
|
entity |
| Predicate | deathPlace |
P21
|
FINISHED |
| Object | Lima |
E2605
|
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: Lima | Statement: [Santa Rosa de Lima, deathPlace, Lima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lima Context triple: [Santa Rosa de Lima, deathPlace, Lima]
-
A.
Lima
chosen
Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
-
B.
Lima
Lima is a subregion of Portugal’s Vinho Verde wine area, known for producing fresh, aromatic white wines from local grape varieties.
-
C.
Lima
Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
-
D.
Sucre
Sucre is the constitutional capital of Bolivia, known for its well-preserved colonial architecture and historical significance in the country’s independence.
-
E.
Chiclayo
Chiclayo is a major commercial and transportation hub in northern Peru, known for its nearby archaeological sites and vibrant regional culture.
- 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_69ca835481fc819084e33d3bc883bfa6 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5826bbb48190a212fb1bb06e05e6 |
completed | March 31, 2026, 11:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf28958ba481908383e31802ce2093 |
completed | April 3, 2026, 2:40 a.m. |
Created at: March 30, 2026, 6:33 p.m.