Triple
T38111280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marunouchi Building |
E951660
|
entity |
| Predicate | isLandmarkNear |
P53174
|
FINISHED |
| Object | Tokyo Station Marunouchi side |
—
|
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: Tokyo Station Marunouchi side | Statement: [Marunouchi Building, isLandmarkNear, Tokyo Station Marunouchi side]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLandmarkNear Context triple: [Marunouchi Building, isLandmarkNear, Tokyo Station Marunouchi side]
-
A.
proximityToLandmark
chosen
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
B.
isLocalLandmark
Indicates that something is recognized as a notable or significant landmark within a specific local area or community.
-
C.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
D.
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.
-
E.
navigationLandmarkFor
Indicates that one entity serves as a reference point or guide used to navigate to or within another entity.
- 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_69f76f065ed08190bdfb1b6d817f5b39 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fcc42cbac48190b8d3e4c9ce140838 |
completed | May 7, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69fcb0fc69c88190800453eb57a7e62c |
completed | May 7, 2026, 3:34 p.m. |
Created at: May 3, 2026, 4:21 p.m.