Triple
T33794354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Justin M. Bibb |
E866025
|
entity |
| Predicate | typeOfLawyer |
P137974
|
FINISHED |
| Object | American attorney |
—
|
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: American attorney | Statement: [Justin M. Bibb, typeOfLawyer, American attorney]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfLawyer Context triple: [Justin M. Bibb, typeOfLawyer, American attorney]
-
A.
legalProfessionType
chosen
Indicates the specific category or type of legal profession associated with an entity (such as lawyer, judge, or notary).
-
B.
typeOfLaw
Indicates that one entity is a specific category or kind of law to which the other entity pertains.
-
C.
typeOfJurist
Indicates that one entity is a specific kind or category of jurist in relation to another entity.
-
D.
legalCareerType
Indicates the specific category or nature of a person’s professional role or trajectory within the legal field.
-
E.
legalProfessionIn
Indicates that an entity practices or holds a legal profession within a specified jurisdiction or geographic area.
- 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_69f3498f99f481909cb271f4965a7594 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a00cda99c908190980e0bf54cb2e2a4 |
completed | May 10, 2026, 6:25 p.m. |
| PD | Predicate disambiguation | batch_6a00cd1635b08190a791ecfcf87a1d54 |
completed | May 10, 2026, 6:23 p.m. |
Created at: May 1, 2026, 1:46 a.m.