Triple
T21597129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Datem del Marañón Province |
E532929
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | San Lorenzo |
—
|
NE NERFINISHED |
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: San Lorenzo | Statement: [Datem del Marañón Province, hasSettlement, San Lorenzo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Lorenzo Context triple: [Datem del Marañón Province, hasSettlement, San Lorenzo]
-
A.
San Lorenzo
San Lorenzo is a historic church in the Italian town of Spello, known for its medieval architecture and religious significance.
-
B.
San Lorenzo
San Lorenzo is an upscale commercial and residential district in Makati, Metro Manila, known for its gated villages, shopping centers, and proximity to the central business area.
-
C.
San Lorenzo
San Lorenzo is an unincorporated community in Alameda County, California, located in the East Bay region of the San Francisco Bay Area.
-
D.
San Lorenzo
San Lorenzo is a rural municipality in Nicaragua’s central Boaco Department, known for its agricultural economy and small-town character.
-
E.
San Lorenzo
chosen
San Lorenzo is a town located in Peru's Loreto Department within the Amazon rainforest region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46364608190a337dc8720dc2a35 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefae20c8881909c5354313d06183a |
completed | April 27, 2026, 5:57 a.m. |
Created at: April 16, 2026, 6:32 p.m.