Triple
T14410786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esquire |
E357317
|
entity |
| Predicate | professionalContext |
P65275
|
FINISHED |
| Object | legal correspondence |
—
|
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: legal correspondence | Statement: [Esquire, professionalContext, legal correspondence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalContext Context triple: [Esquire, professionalContext, legal correspondence]
-
A.
professional
Indicates that one entity has a formal, occupation-related role, service, or expertise in relation to another entity.
-
B.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
C.
professionalSector
Indicates the industry or field in which an entity conducts its professional or occupational activities.
-
D.
professionalScope
Indicates the range of activities, responsibilities, or roles that fall within a person’s or organization’s recognized professional duties or expertise.
-
E.
professionalEnvironment
chosen
Indicates a relationship where an entity operates, interacts, or exists within a work-related or career-oriented setting or context.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90c9b3448190aec1608836a5e913 |
completed | April 14, 2026, 7:08 p.m. |
| PD | Predicate disambiguation | batch_69de2aa1b57881909a033eac8545c417 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:17 a.m.