Triple
T28498853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Metropolitan Routes |
E721178
|
entity |
| Predicate | includesSuburbanSections |
P15571
|
FINISHED |
| Object | Tama area of Tokyo |
—
|
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: Tama area of Tokyo | Statement: [Tokyo Metropolitan Routes, includesSuburbanSections, Tama area of Tokyo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesSuburbanSections Context triple: [Tokyo Metropolitan Routes, includesSuburbanSections, Tama area of Tokyo]
-
A.
hasSuburbanSection
chosen
Indicates that a larger route, line, or area includes a portion that passes through or serves a suburban region.
-
B.
includesSuburbanSystem
Indicates that one transportation or administrative system contains or encompasses a suburban system as a component or subset.
-
C.
hasSuburbanAreas
Indicates that a place includes or is associated with surrounding residential suburban districts or neighborhoods.
-
D.
isSuburbanFocused
Indicates a focus, orientation, or specialization toward suburban areas, communities, or contexts.
-
E.
majorSuburbsIncluded
Indicates that certain major suburbs are encompassed within or form part of a larger defined area or 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_69f01a5afdac8190ac6e72d5c100bd58 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69ff069ec1348190815375c5c9e38404 |
completed | May 9, 2026, 10:04 a.m. |
| PD | Predicate disambiguation | batch_69ff05ba57f88190a45d20f18044e0fb |
completed | May 9, 2026, 10 a.m. |
Created at: April 28, 2026, 3:05 a.m.