Triple
T8740591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Museum of Finland |
E207488
|
entity |
| Predicate | hasLandmarkFeature |
P22581
|
FINISHED |
| Object | tall tower with copper roof |
—
|
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: tall tower with copper roof | Statement: [National Museum of Finland, hasLandmarkFeature, tall tower with copper roof]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandmarkFeature Context triple: [National Museum of Finland, hasLandmarkFeature, tall tower with copper roof]
-
A.
isLandmarkFor
Indicates that one entity serves as a notable or significant reference point or attraction for another entity, such as a place, route, or area.
-
B.
isLocalLandmark
Indicates that something is recognized as a notable or significant landmark within a specific local area or community.
-
C.
includesLandmark
chosen
Indicates that one location or area contains or encompasses a specific landmark within its boundaries.
-
D.
hasCentralLandmark
Indicates that a place or area contains a primary or defining landmark located at or near its center.
-
E.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity 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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d4a0cf481909c770cb39fd00fcd |
completed | March 31, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69cc457322b481908712a9630a17b954 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:38 p.m.