Triple
T13632392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States House of Representatives seat from Florida |
E325753
|
entity |
| Predicate | stateFramework |
P111380
|
FINISHED |
| Object | laws of the state of Florida |
—
|
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: laws of the state of Florida | Statement: [United States House of Representatives seat from Florida, stateFramework, laws of the state of Florida]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stateFramework Context triple: [United States House of Representatives seat from Florida, stateFramework, laws of the state of Florida]
-
A.
stateForm
Indicates that an entity has the political or constitutional form of a particular state or system of government.
-
B.
stateProgram
Indicates that a program is organized, funded, or administered by a governmental state authority.
-
C.
stateRock
Indicates that an entity is in a solid, rock-like physical state or condition.
-
D.
stateUnit
Indicates a relationship where one entity is a constituent administrative or organizational unit within a larger state or state-like structure.
-
E.
stateClassification
Indicates how something is categorized or classified within a particular state or status.
- F. None of above. chosen
Provenance (4 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_69d8076beddc8190a53156f5bea77f5e |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc60635d08190899806fe8936f02a |
completed | April 12, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69dbbe85e1c4819095194f4b7f9f6118 |
completed | April 12, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69dbc6043e148190a2a25f929cfa35e5 |
completed | April 12, 2026, 4:19 p.m. |
Created at: April 9, 2026, 9:51 p.m.