Triple
T28141990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sir Peter Colleton |
E714365
|
entity |
| Predicate | relativeInvolvedIn |
P130291
|
FINISHED |
| Object | Carolina proprietorship |
—
|
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: Carolina proprietorship | Statement: [Sir Peter Colleton, relativeInvolvedIn, Carolina proprietorship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeInvolvedIn Context triple: [Sir Peter Colleton, relativeInvolvedIn, Carolina proprietorship]
-
A.
relativeInvolvedInEvent
chosen
Indicates that a person’s relative participates in, is affected by, or is otherwise involved in a particular event.
-
B.
closelyInvolvedWith
Indicates a relationship in which one entity is deeply and actively engaged with another’s activities, decisions, or affairs.
-
C.
rightInvolved
Indicates that an entity is involved in or associated with a legal or formal right held, exercised, or affected in a given context.
-
D.
hasBeenInvolvedIn
Indicates that an entity has participated in, taken part in, or been connected to a particular event, activity, or situation.
-
E.
mayBeInvolvedIn
Indicates that an entity has a possible, but not certain, participation or role in a particular event, activity, or situation.
- 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_69efd6af156c81908f50c2cd7db0e1ef |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69ffc89596d08190b97bd60b45c7f9c0 |
completed | May 9, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69ffc81ba5dc8190ae94d44e2284948f |
completed | May 9, 2026, 11:49 p.m. |
Created at: April 27, 2026, 9:54 p.m.