Triple
T3928152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walnut Tree Court |
E93327
|
entity |
| Predicate | hasGroundSurface |
P7001
|
FINISHED |
| Object | grass |
—
|
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: grass | Statement: [Walnut Tree Court, hasGroundSurface, grass]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGroundSurface Context triple: [Walnut Tree Court, hasGroundSurface, grass]
-
A.
usesGroundSystem
Indicates that one entity operates, relies on, or interacts with a particular ground-based system to perform its functions or services.
-
B.
hasGroundState
Indicates that an entity possesses a lowest-energy, most stable state in its energy configuration.
-
C.
hasGrounds
chosen
Indicates that one entity possesses or includes a physical area of land or outdoor space associated with it.
-
D.
hasLandComponent
Indicates that something includes, consists of, or is associated with a land-based part or portion as one of its components.
-
E.
ground
Indicates that one entity is in contact with or supported by the ground or a ground-like surface.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeeda4f9d481908dda1b5a826ab64d |
completed | March 9, 2026, 3:56 p.m. |
| PD | Predicate disambiguation | batch_69aee7609c4081908000ce12ae827c3f |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:23 p.m.