Triple
T11209327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M-59 (Michigan highway) |
E265261
|
entity |
| Predicate | hasSegmentConfiguration |
P2563
|
FINISHED |
| Object | surface arterial road |
—
|
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: surface arterial road | Statement: [M-59 (Michigan highway), hasSegmentConfiguration, surface arterial road]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSegmentConfiguration Context triple: [M-59 (Michigan highway), hasSegmentConfiguration, surface arterial road]
-
A.
hasSegmentOn
Indicates that one entity includes or occupies a specific segment or portion on another entity (such as a line, path, or sequence).
-
B.
hasConfiguration
Indicates that an entity is associated with or defined by a particular configuration or setup.
-
C.
hasSegmentType
chosen
Indicates that an entity is associated with, or classified by, a particular type or category of segment within a larger structure or sequence.
-
D.
hasMultipleSegments
Indicates that the referenced entity is composed of more than one distinct segment or section.
-
E.
hasUserSegment
Indicates that an entity is associated with a particular user segment or group of users defined by shared characteristics or behaviors.
- 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8d5f8908190903817f84c629ba1 |
completed | April 9, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69d75cf83464819087529d47d025d313 |
completed | April 9, 2026, 8:02 a.m. |
Created at: April 8, 2026, 9:30 p.m.