Triple
T6176159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Rachel Maddow Show |
E137823
|
entity |
| Predicate | hasHostNationality |
P24717
|
FINISHED |
| Object | American |
—
|
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: American | Statement: [The Rachel Maddow Show, hasHostNationality, American]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHostNationality Context triple: [The Rachel Maddow Show, hasHostNationality, American]
-
A.
hostNationality
chosen
Indicates the national affiliation or citizenship of the host in a given hosting relationship or context.
-
B.
bearerNationality
Indicates that one entity is the country or nationality associated with the bearer of another entity, such as a document or credential.
-
C.
appliesToPersonNationality
Indicates that something is relevant or applicable specifically to a person’s nationality.
-
D.
hasTypicalCitizenship
Indicates that an entity is generally or commonly a citizen of a specified country or jurisdiction.
-
E.
includedNationality
Indicates that one entity’s set of nationalities contains or encompasses the nationality of 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_69c008a80f748190ba3d07ffc81acb29 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05dc70ac481909fd1db0d69837eca |
completed | March 22, 2026, 9:23 p.m. |
| PD | Predicate disambiguation | batch_69c055f7f12881908e21c04e9b752ba4 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:18 p.m.