Triple
T32829089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parson's Cause case involving Patrick Henry |
E839638
|
entity |
| Predicate | occupationOfParticipant |
P83547
|
FINISHED |
| Object | Patrick Henry – lawyer |
—
|
NE NERFINISHED |
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: Patrick Henry – lawyer | Statement: [Parson's Cause case involving Patrick Henry, occupationOfParticipant, Patrick Henry – lawyer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationOfParticipant Context triple: [Parson's Cause case involving Patrick Henry, occupationOfParticipant, Patrick Henry – lawyer]
-
A.
occupationOf
Indicates that one entity holds or performs the job, role, or profession associated with another entity.
-
B.
occupationOfBearer
Indicates that a specified occupation or job role is held by the bearer entity.
-
C.
occupationDuringAlias
Indicates that an entity held a particular occupation specifically during the time period when it was known by a given alias.
-
D.
occupationOfAssociatedPerson
chosen
Indicates the job or professional role held by a person who is associated with another referenced entity.
-
E.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
- 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_69f3493f22f88190ae6dd4bc15b6cf8d |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d16f5cb881908eed141afaaa0b51 |
completed | May 3, 2026, 4:39 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:16 a.m.