Triple
T21596695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Aid Road Act of 1916 |
E532918
|
entity |
| Predicate | matchingRatio |
P86776
|
FINISHED |
| Object | 50 percent federal, 50 percent state |
—
|
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: 50 percent federal, 50 percent state | Statement: [Federal Aid Road Act of 1916, matchingRatio, 50 percent federal, 50 percent state]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: matchingRatio Context triple: [Federal Aid Road Act of 1916, matchingRatio, 50 percent federal, 50 percent state]
-
A.
matchRatio
chosen
Indicates the degree of similarity or correspondence between two items, typically expressed as a numerical ratio or percentage.
-
B.
matchType
Indicates the specific category or nature of how two or more entities correspond or align with each other within a given context.
-
C.
significantMatch
Indicates that two entities correspond closely or share a notably strong degree of similarity or relevance to each other.
-
D.
matches
Indicates that two entities correspond to or are in agreement with each other according to some defined criteria or pattern.
-
E.
matchLevel
Indicates the degree or extent to which two entities correspond, align, or are compatible with each other.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefae20c8881909c5354313d06183a |
completed | April 27, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e632109d048190b4ac3f14fe48d1a0 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:32 p.m.