Triple
T27809860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Critter Country (Disneyland) |
E702488
|
entity |
| Predicate | featuresWaterfront |
P114920
|
FINISHED |
| Object | Rivers of America |
—
|
NE NERFINISHED |
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: Rivers of America | Statement: [Critter Country (Disneyland), featuresWaterfront, Rivers of America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresWaterfront Context triple: [Critter Country (Disneyland), featuresWaterfront, Rivers of America]
-
A.
hasWaterfrontType
Indicates that an entity is associated with a specific type or category of waterfront (e.g., oceanfront, lakefront, riverfront).
-
B.
hasWaterfrontView
Indicates that a property or location offers a direct view of a body of water from its premises.
-
C.
hasWaterfrontUse
Indicates that an entity is used, designated, or suitable for activities or purposes directly related to a waterfront or shoreline area.
-
D.
associatedWithWaterfront
chosen
Indicates a relationship in which something is located on, adjacent to, or otherwise directly connected with a waterfront area.
-
E.
hasWaterFeatures
Indicates that an entity includes or is associated with water-related elements such as fountains, ponds, streams, or similar features.
- 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_69ef840a16748190926719ab96120bae |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69fd9ff026a48190bfec33deeb3b2c43 |
completed | May 8, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69fd97d805bc8190ba12f429d3ad04c7 |
completed | May 8, 2026, 7:59 a.m. |
Created at: April 27, 2026, 5:41 p.m.