Triple
T15136326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detroit area freeway network |
E361563
|
entity |
| Predicate | includesUrbanSegment |
P11388
|
FINISHED |
| Object | Downtown Detroit freeway loop |
—
|
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: Downtown Detroit freeway loop | Statement: [Detroit area freeway network, includesUrbanSegment, Downtown Detroit freeway loop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesUrbanSegment Context triple: [Detroit area freeway network, includesUrbanSegment, Downtown Detroit freeway loop]
-
A.
isUrbanSectionOf
Indicates that one area or segment is the part of a larger entity that lies within an urban or city environment.
-
B.
isUrbanSee
Indicates a relationship where a location or area is recognized or classified as an urban settlement or city-like environment.
-
C.
containsUrbanArea
chosen
Indicates that a geographic region fully or partially encompasses an urbanized area within its boundaries.
-
D.
containsUrbanForm
Indicates that one entity spatially includes or encompasses an urban form or built-up area within its extent.
-
E.
isUrbanRoute
Indicates that a route is located within, passes through, or primarily serves an urban or metropolitan area.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005b3f6f48190b1ed7c7b28feb7a6 |
completed | April 15, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69deb9713fe881909dec2fd3f6c84b39 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:07 a.m.