Triple
T37428723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jizō-dōri |
E930068
|
entity |
| Predicate | hasStreetLength |
P53813
|
FINISHED |
| Object | approximately 800 meters |
—
|
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: approximately 800 meters | Statement: [Jizō-dōri, hasStreetLength, approximately 800 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStreetLength Context triple: [Jizō-dōri, hasStreetLength, approximately 800 meters]
-
A.
roadLength
chosen
Indicates the measured distance or extent of a road, typically expressed in units of length.
-
B.
hasShoppingStreetLength
Indicates the length or extent of a shopping street associated with a given place or area.
-
C.
hasNumberOfStreets
Indicates the relationship that specifies how many streets are associated with or contained within a given entity.
-
D.
hasStreet
Indicates that an entity is located on, associated with, or identified by a particular street.
-
E.
hasMainStraightLengthKm
Indicates the length in kilometers of the primary or main straight segment associated with an 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_69f76ebf0f288190ba198a78341613b8 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a007899cadc8190a04edd503eaf6514 |
completed | May 10, 2026, 12:22 p.m. |
| PD | Predicate disambiguation | batch_6a0078493e088190b0c5047cbe75d304 |
completed | May 10, 2026, 12:21 p.m. |
Created at: May 3, 2026, 4:16 p.m.