Triple
T21091812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Survivors’ and Dependents’ Educational Assistance |
E519655
|
entity |
| Predicate | legalChapter |
P57251
|
FINISHED |
| Object | Chapter 35 |
—
|
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: Chapter 35 | Statement: [Survivors’ and Dependents’ Educational Assistance, legalChapter, Chapter 35]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalChapter Context triple: [Survivors’ and Dependents’ Educational Assistance, legalChapter, Chapter 35]
-
A.
chapterOfLaw
chosen
Indicates that one legal text or section is a chapter belonging to a specific law or legal act.
-
B.
legalContent
Indicates that the associated material complies with applicable laws and regulations and is permitted for use, distribution, or display.
-
C.
legalDetail
Indicates that there is specific legal information, conditions, or attributes associated with the related entity or relationship.
-
D.
legalCodeFocus
Indicates that something is specifically concerned with, centered on, or primarily addressing a particular legal code or body of law.
-
E.
lawCharacteristicInText
Indicates that a specific legal characteristic or feature is expressed, described, or referenced within a given text.
- 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_69e0b507dd9081908fb8bfcbef4c8b46 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7094f6ebc8190a90b014755a9d4a6 |
completed | April 21, 2026, 5:21 a.m. |
| PD | Predicate disambiguation | batch_69e5dbfcd5e881908f1e4e0d2d237856 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:51 p.m.