Triple
T4899702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garrett Park, Maryland |
E109767
|
entity |
| Predicate | hasTreeCanopyProtection |
P60548
|
FINISHED |
| Object | strong tree preservation ordinances |
—
|
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: strong tree preservation ordinances | Statement: [Garrett Park, Maryland, hasTreeCanopyProtection, strong tree preservation ordinances]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTreeCanopyProtection Context triple: [Garrett Park, Maryland, hasTreeCanopyProtection, strong tree preservation ordinances]
-
A.
hasCanopyDensity
Indicates the degree to which a canopy (such as a tree or forest cover) occupies or obscures the area beneath it.
-
B.
hasCanopy
Indicates that one entity possesses or is characterized by a canopy associated with it.
-
C.
hasTrees
Indicates that something possesses or contains one or more trees.
-
D.
hasForestType
Indicates that an area or location is characterized by a specific type or classification of forest.
-
E.
hasTreeVigor
Indicates the assessed strength, health, and growth potential of a tree in relation to its current condition or environment.
- F. None of above. chosen
Provenance (4 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_69bd4410bbf88190aad50d2451c863d6 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd706245e48190a61d573438461c30 |
completed | March 20, 2026, 4:05 p.m. |
| PD | Predicate disambiguation | batch_69bd6c306b188190a08a7856beb76db4 |
completed | March 20, 2026, 3:48 p.m. |
| PDg | Predicate description generation | batch_69bd7060f9988190afdf98eb0a38515d |
completed | March 20, 2026, 4:05 p.m. |
Created at: March 20, 2026, 1:28 p.m.