Triple
T34280709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York |
E879581
|
entity |
| Predicate | wasPlaceOfLegalCareerOf |
P2755
|
FINISHED |
| Object | Samuel Nelson |
—
|
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: Samuel Nelson | Statement: [New York, wasPlaceOfLegalCareerOf, Samuel Nelson]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasPlaceOfLegalCareerOf Context triple: [New York, wasPlaceOfLegalCareerOf, Samuel Nelson]
-
A.
practicedLawIn
chosen
Indicates that a person engaged in the professional practice of law within a specified jurisdiction or location.
-
B.
legalProfessionRole
Indicates that one entity holds or performs a specific professional role within the legal domain in relation to another entity or context.
-
C.
employsAsLawyer
Indicates that one entity hires or retains another entity specifically to provide legal representation or services as a lawyer.
-
D.
legalProfessionIncludes
Indicates that a legal profession or role encompasses, involves, or includes another specified legal function, specialization, or activity.
-
E.
legalProfessionType
Indicates the specific category or type of legal profession associated with an entity (such as lawyer, judge, or notary).
- 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_69f349b5f6648190b9420d94a4cd16e0 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71362f1448190985a80ce7af475cb |
completed | May 3, 2026, 9:20 a.m. |
| PD | Predicate disambiguation | batch_69f7127884388190884f23d181a65d19 |
completed | May 3, 2026, 9:16 a.m. |
Created at: May 1, 2026, 1:57 a.m.