Triple
T6377680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Public Law 107-243 |
E143504
|
entity |
| Predicate | section2Content |
P2254
|
FINISHED |
| Object | findings and policy |
—
|
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: findings and policy | Statement: [Public Law 107-243, section2Content, findings and policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: section2Content Context triple: [Public Law 107-243, section2Content, findings and policy]
-
A.
section2Description
Indicates the descriptive text or content provided for the second section of a structured item or document.
-
B.
textOfSection2
chosen
Indicates that one entity is the textual content that constitutes the second section of another entity.
-
C.
section3ContentSummary
Indicates a summarized representation of the main points or key information contained in the third section of a document or structure.
-
D.
section5Description
Indicates a descriptive text or explanation specifically associated with section 5 of a document or structured content.
-
E.
module2Content
Indicates that one entity contains or provides the instructional or informational material that makes up the second module in a sequence or course.
- 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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0683d7af881908d66d5230e1bfcb6 |
completed | March 22, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69c060eff524819094cee1c70a0c1ff4 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:33 p.m.