Triple
T5506286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | APSA Hubert H. Humphrey Award |
E144447
|
entity |
| Predicate | hasNamesakeRole |
P5041
|
FINISHED |
| Object | U.S. Senator from Minnesota |
—
|
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. Senator from Minnesota | Statement: [APSA Hubert H. Humphrey Award, hasNamesakeRole, U.S. Senator from Minnesota]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNamesakeRole Context triple: [APSA Hubert H. Humphrey Award, hasNamesakeRole, U.S. Senator from Minnesota]
-
A.
hasFamousNamesakeRole
chosen
Indicates that an entity has a role or position that shares its name with a well-known or historically notable person.
-
B.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
C.
namedPersonRole
Indicates that a person is identified by name as holding a specific role or position in a given context.
-
D.
hasHistoricalRoleAs
Indicates that an entity has served in a specific historical capacity, function, or position during a particular period or context.
-
E.
famousRole
Indicates that an entity is best known for portraying or performing a particular role or character.
- 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f47d2dc8190ad874be6902d8a4c |
completed | March 22, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69c01b07bde08190b3933b96bdc70dd5 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:32 p.m.