Triple
T26066666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baron Passfield |
E657420
|
entity |
| Predicate | grantedToOccupation |
P163045
|
FINISHED |
| Object | politician |
—
|
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: politician | Statement: [Baron Passfield, grantedToOccupation, politician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grantedToOccupation Context triple: [Baron Passfield, grantedToOccupation, politician]
-
A.
allowedOccupationOf
Indicates that one entity is permitted or authorized to hold or perform the occupation associated with another entity.
-
B.
hadOccupationAuthority
Indicates that an entity held an official position or role that granted them recognized authority or power within an occupation or professional context.
-
C.
requiredOccupationOf
Indicates that one entity specifies the occupation or job role that is required or expected for another entity (such as a position, task, or qualification).
-
D.
endedOccupationOf
Indicates that one entity brought another entity’s occupation or control of a place or position to an end.
-
E.
derivesFromOccupation
Indicates that one entity originates from, is obtained through, or is a result of another entity’s occupation or professional role.
- 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_69ee5bbe539081909efc7f9dd7c1b53c |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f63639a84c81909d700a539b458b42 |
completed | May 2, 2026, 5:36 p.m. |
| PD | Predicate disambiguation | batch_69f63182f1408190bddc1214fcbd6145 |
completed | May 2, 2026, 5:16 p.m. |
| PDg | Predicate description generation | batch_69f6352df6148190bc10772cd40bd7b3 |
completed | May 2, 2026, 5:32 p.m. |
Created at: April 26, 2026, 7:24 p.m.