Triple
T8405221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turin Township, Michigan |
E198478
|
entity |
| Predicate | hasForestedLandscape |
P71211
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Turin Township, Michigan, hasForestedLandscape, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasForestedLandscape Context triple: [Turin Township, Michigan, hasForestedLandscape, true]
-
A.
isForested
chosen
Indicates that an area or region is covered predominantly by forest or dense tree vegetation.
-
B.
hasForestType
Indicates that an area or location is characterized by a specific type or classification of forest.
-
C.
hasForestedSlopes
Indicates that the subject has slopes that are covered predominantly with forest or woodland vegetation.
-
D.
forestCoverCharacteristic
Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
-
E.
isUrbanForest
Indicates that an area of trees and vegetation is located within or closely integrated with an urban or suburban environment.
- 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_69ca8310df9c8190b25f16161cca3e41 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83116bf48190894bd5d5465520ef |
completed | March 31, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69cb70d473dc8190af8ea81ee5aa970d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:05 p.m.