Triple
T10251618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Wolfe statue |
E240355
|
entity |
| Predicate | hasViewpointStatus |
P93193
|
FINISHED |
| Object | popular tourist viewpoint |
—
|
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: popular tourist viewpoint | Statement: [General Wolfe statue, hasViewpointStatus, popular tourist viewpoint]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasViewpointStatus Context triple: [General Wolfe statue, hasViewpointStatus, popular tourist viewpoint]
-
A.
hasViewpointType
Indicates that something is associated with or characterized by a particular type or category of viewpoint or perspective.
-
B.
hasView
Indicates that one entity provides a visual perspective or outlook onto another entity or scene.
-
C.
viewpointFrom
Indicates a relationship where something is observed, depicted, or described from the perspective or location of a particular entity or point.
-
D.
hasHistoricalViewpoint
Indicates that one entity holds, expresses, or is associated with a particular perspective or interpretation regarding historical events or periods.
-
E.
seeStatus
Indicates that one entity observes or becomes aware of the current state or condition of another entity or process.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d328272c8190a3548d7f7f38cfc4 |
completed | April 7, 2026, 9:49 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ebd6c88190a1f3f4a72a99d6fe |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d32741888190928b045e2241cfac |
completed | April 7, 2026, 9:49 a.m. |
Created at: April 6, 2026, 11:29 a.m.