Triple
T33265769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Demons |
E851640
|
entity |
| Predicate | associatedWithLegalField |
P113669
|
FINISHED |
| Object | international 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: international law | Statement: [Lieutenant Demons, associatedWithLegalField, international law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithLegalField Context triple: [Lieutenant Demons, associatedWithLegalField, international law]
-
A.
appliesToFieldOfLaw
chosen
Indicates that something is relevant or applicable to a particular field or branch of law.
-
B.
legalProfessionIncludes
Indicates that a legal profession or role encompasses, involves, or includes another specified legal function, specialization, or activity.
-
C.
associatedWithJuristSchool
Indicates that an entity is connected or affiliated with a particular juristic or legal school of thought.
-
D.
legalProfessionIn
Indicates that an entity practices or holds a legal profession within a specified jurisdiction or geographic area.
-
E.
associatedWithJurisdiction
Indicates that an entity has a formal or relevant connection to a particular legal or administrative jurisdiction.
- 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_69f349642dac81908a37ffcc3b976a55 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:32 a.m.