Triple
T2749703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rainbow Bridge |
E60954
|
entity |
| Predicate | connectsLandform |
P41624
|
FINISHED |
| Object | mainland Tokyo |
—
|
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: mainland Tokyo | Statement: [Rainbow Bridge, connectsLandform, mainland Tokyo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsLandform Context triple: [Rainbow Bridge, connectsLandform, mainland Tokyo]
-
A.
hasLandform
Indicates that one entity possesses, contains, or is characterized by a particular natural landform.
-
B.
geologicalFormation
Indicates that one entity is a geological structure or feature that characterizes or composes the physical makeup of another entity.
-
C.
terrainFeature
Indicates a relationship where one entity is a natural or constructed landform or surface characteristic associated with a given location or area.
-
D.
connectsBodyOfWater
Indicates a relationship where one entity serves as a link or passage between two bodies of water, allowing them to be joined or interact.
-
E.
connectsRegions
Indicates a relationship where one entity links or joins two or more distinct regions, enabling passage, interaction, or continuity between them.
- F. None of above. chosen
Provenance (4 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_69ab4b79846081909096725374d65ce9 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb52b1d48190b6ab511cc8834a14 |
completed | March 7, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69abd82d005c81908a1ac7a1313c6d88 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd91497ec8190927e91ad33549eda |
completed | March 7, 2026, 7:51 a.m. |
Created at: March 6, 2026, 9:56 p.m.