Triple
T36044545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gardar Cathedral |
E1042631
|
entity |
| Predicate | nearbyStructures |
P145550
|
FINISHED |
| Object | bishop’s farm |
—
|
LITERAL 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: bishop’s farm | Statement: [Gardar Cathedral, nearbyStructures, bishop’s farm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyStructures Context triple: [Gardar Cathedral, nearbyStructures, bishop’s farm]
-
A.
hasNearbyInfrastructureType
Indicates that an entity is located close to infrastructure of a specified type.
-
B.
infrastructureNearby
Indicates that one entity is located close to another entity that serves as infrastructure (such as roads, utilities, or public facilities).
-
C.
builtNear
Indicates that one entity was constructed at a location geographically close to another entity.
-
D.
nearbyNotableObject
chosen
Indicates that one entity is a notable or significant object located close to another entity in space.
-
E.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
- 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_69f76e2e41f8819091f9fb0536920fec |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fed6da0390819096b88ef4714b144e |
completed | May 9, 2026, 6:40 a.m. |
| PD | Predicate disambiguation | batch_69fed53517d081909966f31707625f1a |
completed | May 9, 2026, 6:33 a.m. |
Created at: May 3, 2026, 4:07 p.m.