Triple
T13969102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Interstate 5 corridor (west side suburbs) |
E336004
|
entity |
| Predicate | landUseMix |
P38195
|
FINISHED |
| Object | residential |
—
|
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: residential | Statement: [Interstate 5 corridor (west side suburbs), landUseMix, residential]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landUseMix Context triple: [Interstate 5 corridor (west side suburbs), landUseMix, residential]
-
A.
landUseContext
Indicates the contextual setting or circumstances under which a particular piece of land is used or designated for a specific purpose.
-
B.
hasUrbanRuralMix
Indicates that something exhibits a combination or blend of both urban and rural characteristics or components.
-
C.
landUseIncludes
Indicates that a specified land area contains or permits the specified type(s) of land use within its boundaries.
-
D.
propertyTypeMix
Indicates that an entity involves or consists of a combination of different types of properties rather than a single uniform property type.
-
E.
majorLandUse
chosen
Indicates the primary way a given area of land is utilized or designated (e.g., residential, commercial, agricultural).
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8daeac8190aadd4b3b60222482 |
completed | April 14, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69dd465a21408190b912a42c50ffa0d9 |
completed | April 13, 2026, 7:39 p.m. |
Created at: April 9, 2026, 10:18 p.m.