Triple
T16093610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belmont (estate of John Perkins Cushing) |
E390423
|
entity |
| Predicate | hasNotableOwnerOccupation |
P22136
|
FINISHED |
| Object | China trader |
—
|
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: China trader | Statement: [Belmont (estate of John Perkins Cushing), hasNotableOwnerOccupation, China trader]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableOwnerOccupation Context triple: [Belmont (estate of John Perkins Cushing), hasNotableOwnerOccupation, China trader]
-
A.
notableOwnerOccupation
chosen
Indicates that the occupation of an owner is particularly notable or significant in the context of the ownership relationship.
-
B.
ownerOccupation
Indicates that the occupation or job role of an entity that owns something is being specified or described.
-
C.
hasNotableOwner
Indicates that an entity is or has been owned by a person or organization considered notable or significant.
-
D.
hasNonHumanResident
Indicates that a place or location is inhabited or occupied by one or more non-human entities.
-
E.
ownsOrLeases
Indicates that one entity has legal rights to use or control another entity either through ownership or through a lease agreement.
- 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e182804208819087f35307cd6e4103 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:59 a.m.