Triple
T6091395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Stanford Review |
E135773
|
entity |
| Predicate | notableAlumnus |
P304
|
FINISHED |
| Object |
Josh Hawley
Josh Hawley is a conservative American politician and lawyer serving as the junior United States senator from Missouri.
|
E569804
|
NE FINISHED |
How this triple was built (4 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: Josh Hawley | Statement: [The Stanford Review, notableAlumnus, Josh Hawley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Josh Hawley Context triple: [The Stanford Review, notableAlumnus, Josh Hawley]
-
A.
Richard Hudson
Richard Hudson is the child of Katherine Hudson, known primarily in relation to her.
-
B.
Ben Sasse
Ben Sasse is an American academic and politician who served as a U.S. Senator from Nebraska before becoming president of the University of Florida.
-
C.
Tom Watson
Tom Watson is a fictional character associated with Detective Riley, likely serving as a key figure in crime or mystery narratives involving that detective.
-
D.
Tom Watson
Tom Watson is a Scottish actor known for his work in film, television, and theatre, including a role in the drama "The Winter Guest" (1997).
-
E.
Tom Watson
Tom Watson is a central character in the psychological thriller film "The Girl on the Train," depicted as the unfaithful ex-husband whose deceit and manipulation drive much of the story’s suspense and mystery.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Josh Hawley Triple: [The Stanford Review, notableAlumnus, Josh Hawley]
Generated description
Josh Hawley is a conservative American politician and lawyer serving as the junior United States senator from Missouri.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Josh Hawley Target entity description: Josh Hawley is a conservative American politician and lawyer serving as the junior United States senator from Missouri.
-
A.
Richard Hudson
Richard Hudson is the child of Katherine Hudson, known primarily in relation to her.
-
B.
Ben Sasse
Ben Sasse is an American academic and politician who served as a U.S. Senator from Nebraska before becoming president of the University of Florida.
-
C.
Tom Watson
Tom Watson is a fictional character associated with Detective Riley, likely serving as a key figure in crime or mystery narratives involving that detective.
-
D.
Tom Watson
Tom Watson is a Scottish actor known for his work in film, television, and theatre, including a role in the drama "The Winter Guest" (1997).
-
E.
Tom Watson
Tom Watson is a central character in the psychological thriller film "The Girl on the Train," depicted as the unfaithful ex-husband whose deceit and manipulation drive much of the story’s suspense and mystery.
- F. None of above. chosen
Provenance (5 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_69c0087bcc788190b20f093d3a6c60ec |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057ab7324819086d4708e6f9391c0 |
completed | March 22, 2026, 8:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c12531d764819083e1bb37d8c81a6c |
completed | March 23, 2026, 11:34 a.m. |
| NEDg | Description generation | batch_69c12964de988190a9673abe4140980e |
completed | March 23, 2026, 11:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c129d7cd348190963e1a266d5e88ae |
completed | March 23, 2026, 11:53 a.m. |
Created at: March 22, 2026, 4:12 p.m.