Triple
T165578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Sherman Tree |
E3006
|
entity |
| Predicate | hasNearbyFacility |
P5648
|
FINISHED |
| Object | parking area for visitors |
—
|
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: parking area for visitors | Statement: [General Sherman Tree, hasNearbyFacility, parking area for visitors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyFacility Context triple: [General Sherman Tree, hasNearbyFacility, parking area for visitors]
-
A.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
-
B.
nearbyCurrent
Indicates that one entity is located close to another entity at the present moment or in the current context.
-
C.
hasNearbyCommunity
Indicates that one entity has another community located close to it in geographic or spatial terms.
-
D.
hasFacilityType
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
E.
hasNearbyTown
Indicates that one location has a town situated close to it in geographic proximity.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a25883ac8481909616b2179561bd98 |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a25664ba8081908ac298511a9fc5ba |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a256eb46ec81909c730000e5041d0d |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.