Triple
T1226395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Jennifer Hudson Show |
E26335
|
entity |
| Predicate | hostNationality |
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 Jennifer Hudson Show, hostNationality, American]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hostNationality Context triple: [The Jennifer Hudson Show, hostNationality, American]
-
A.
sponsorNationality
Indicates that an entity acts as a sponsor and has the specified nationality.
-
B.
motherNationality
Indicates the country or national identity associated with a person's mother.
-
C.
hostCountryNOC
Indicates that a National Olympic Committee (NOC) is the official host country organization for a particular Olympic Games or sporting event.
-
D.
officeHolderNationality
Indicates that the nationality of an office holder is a specified country or nation.
-
E.
nationalityInStory
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
- 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_69a49484688c8190a1bf285eb396a8b6 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be39908481908cca21aaf0828415 |
completed | March 1, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69a4bb65d61c8190bf0424ea0019a98b |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bbf83584819088c69366f58586cc |
completed | March 1, 2026, 10:21 p.m. |
Created at: March 1, 2026, 7:47 p.m.