Triple
T7772778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | K Street NW |
E179112
|
entity |
| Predicate | hasMetonymicUse |
P71235
|
FINISHED |
| Object | U.S. lobbying industry |
—
|
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: U.S. lobbying industry | Statement: [K Street NW, hasMetonymicUse, U.S. lobbying industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMetonymicUse Context triple: [K Street NW, hasMetonymicUse, U.S. lobbying industry]
-
A.
hasConnotation
Indicates that one entity carries an implied or associated meaning, tone, or emotional nuance in relation to another entity.
-
B.
heteronym
Indicates that two or more words share the same spelling but differ in pronunciation and meaning.
-
C.
isFamouslyUsedIn
Indicates that something is widely recognized or well-known for being used in a particular context, work, or situation.
-
D.
hasCommonAcronymUsage
Indicates that two terms share the same commonly used acronym in practice.
-
E.
oftenUsedAsNameFor
chosen
Indicates that something frequently serves as a name or designation for another entity.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c7046048688190a6cbc64e82b58eca |
completed | March 27, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69c7016f4ce881909c2e9f610255187b |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:11 p.m.