Triple
T7072265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Nourse |
E164726
|
entity |
| Predicate | typeOfCrimeInvolved |
P7957
|
FINISHED |
| Object | slave trading |
—
|
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: slave trading | Statement: [James Nourse, typeOfCrimeInvolved, slave trading]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCrimeInvolved Context triple: [James Nourse, typeOfCrimeInvolved, slave trading]
-
A.
crimeType
chosen
Indicates the specific category or nature of the crime associated with an event or entity.
-
B.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
C.
constantInvolved
Indicates that a constant participates in or is directly involved in the specified relation, operation, or context.
-
D.
targetOfCrime
Indicates that the subject is the person, organization, or entity against whom the referenced crime is committed.
-
E.
roleInCrime
Indicates the specific function, responsibility, or participation an entity has within the commission of a particular crime.
- 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_69c6887b96548190a8a9b3ac8adf4119 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4c9cdbc8190b91cd3b4eef58eb6 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bfcb948190a5ada74fb8c054cb |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:39 p.m.