Triple
T11593127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lodi Township, Michigan |
E274932
|
entity |
| Predicate | hasLandUsePolicyGoal |
P14666
|
FINISHED |
| Object | preservation of agricultural land |
—
|
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: preservation of agricultural land | Statement: [Lodi Township, Michigan, hasLandUsePolicyGoal, preservation of agricultural land]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandUsePolicyGoal Context triple: [Lodi Township, Michigan, hasLandUsePolicyGoal, preservation of agricultural land]
-
A.
hasPolicyGoal
chosen
Indicates that an entity is associated with, or aims to achieve, a specific policy objective or target.
-
B.
hasLandUseCharacter
Indicates that one entity possesses or is associated with a particular type or pattern of land use.
-
C.
hasConservationGoal
Indicates that an entity is associated with or aims to achieve a specific conservation-related objective or target.
-
D.
hasPolicyArea
Indicates that an entity (such as a policy, program, or initiative) is associated with or pertains to a specific policy area or domain.
-
E.
urbanDesignGoal
Indicates a goal or intended outcome related to the planning, shaping, or improvement of urban spaces and environments.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8946594348190935106132fd18028 |
completed | April 10, 2026, 6:10 a.m. |
| PD | Predicate disambiguation | batch_69d85dd20d188190863d1190d4c16048 |
completed | April 10, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:38 p.m.