Triple
T13997630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lindsay Dole |
E336740
|
entity |
| Predicate | hasProfessionFocus |
P55248
|
FINISHED |
| Object | criminal defense law |
—
|
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: criminal defense law | Statement: [Lindsay Dole, hasProfessionFocus, criminal defense law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionFocus Context triple: [Lindsay Dole, hasProfessionFocus, criminal defense law]
-
A.
hasProfessionalOrientation
chosen
Indicates that an entity is directed toward, focused on, or aligned with a particular profession, career field, or occupational domain.
-
B.
hasNotableProfessionField
Indicates that an entity’s notable profession or occupation belongs to a particular professional field or domain.
-
C.
hasProfessionTrait
Indicates that an entity possesses a particular characteristic, quality, or attribute specifically related to their profession or occupational role.
-
D.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
E.
hasProfessionalSection
Indicates that an entity includes or is associated with a designated professional section, division, or category within its structure or content.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2eb68ba88190bfaf10777d607bf3 |
completed | April 14, 2026, 12:10 p.m. |
| PD | Predicate disambiguation | batch_69dd465dfbc4819090d8c61fd572d35f |
completed | April 13, 2026, 7:39 p.m. |
Created at: April 9, 2026, 10:19 p.m.