Triple
T25613481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monument to the Miner |
E642096
|
entity |
| Predicate | hasAssociatedTown |
P847
|
FINISHED |
| Object | Real del Monte |
—
|
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: Real del Monte | Statement: [Monument to the Miner, hasAssociatedTown, Real del Monte]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedTown Context triple: [Monument to the Miner, hasAssociatedTown, Real del Monte]
-
A.
hasAssociatedCity
Indicates that one entity is linked or related to a specific city, typically as its location, base, or primary area of association.
-
B.
hasTown
chosen
Indicates that one entity possesses, contains, or is associated with a town as part of its structure, jurisdiction, or composition.
-
C.
hasTownship
Indicates that one administrative area or jurisdiction includes or is associated with a specific township.
-
D.
hasNearbyTown
Indicates that one location has a town situated close to it in geographic proximity.
-
E.
hasNearbyTownType
Indicates that one entity has, in its vicinity, a town of a specified type or classification.
- F. None of above.
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_69e75dc6ccf081908d49578fd36a76d5 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f621fcea1481909b6f8b3af1ee6820 |
completed | May 2, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69f620dc38088190b56b2b15ed75b3c2 |
completed | May 2, 2026, 4:05 p.m. |
Created at: April 21, 2026, 4:42 p.m.