Triple
T23000980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Hayes |
E572630
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Kate Shaw
Kate Shaw is an American legal scholar, law professor, and media commentator known for her expertise in constitutional and administrative law.
|
E1565496
|
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: Kate Shaw | Statement: [Chris Hayes, spouse, Kate Shaw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Shaw Context triple: [Chris Hayes, spouse, Kate Shaw]
-
A.
Susan Shaw
Susan Shaw was a British film actress known for her roles in post-war cinema, particularly in crime dramas and social realist films.
-
B.
Kim Shaw
Kim Shaw is a Canadian-born American actress known for her work in film and television, including roles in projects like the 2016 movie "Christine."
-
C.
Sarah Shayne
Sarah Shayne is a character from the soap opera "Guiding Light," known primarily as the mother of Reva Shayne.
-
D.
Heather Shaw
Heather Shaw is the central protagonist of the British supernatural thriller series "The Rig," a crew member on a remote North Sea oil platform who confronts mysterious and dangerous phenomena threatening her team.
-
E.
Mary Shaw
Mary Shaw is a prominent American computer scientist renowned for her pioneering contributions to software architecture and software engineering research.
- 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: Kate Shaw Triple: [Chris Hayes, spouse, Kate Shaw]
Generated description
Kate Shaw is an American legal scholar, law professor, and media commentator known for her expertise in constitutional and administrative law.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kate Shaw Target entity description: Kate Shaw is an American legal scholar, law professor, and media commentator known for her expertise in constitutional and administrative law.
-
A.
Susan Shaw
Susan Shaw was a British film actress known for her roles in post-war cinema, particularly in crime dramas and social realist films.
-
B.
Kim Shaw
Kim Shaw is a Canadian-born American actress known for her work in film and television, including roles in projects like the 2016 movie "Christine."
-
C.
Sarah Shayne
Sarah Shayne is a character from the soap opera "Guiding Light," known primarily as the mother of Reva Shayne.
-
D.
Heather Shaw
Heather Shaw is the central protagonist of the British supernatural thriller series "The Rig," a crew member on a remote North Sea oil platform who confronts mysterious and dangerous phenomena threatening her team.
-
E.
Mary Shaw
Mary Shaw is a prominent American computer scientist renowned for her pioneering contributions to software architecture and software engineering research.
- 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_69e245b6a3ac81908087599eefe3e365 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18352bcf48190bd474eff69b3465b |
completed | April 29, 2026, 4:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bd380d84c8190ad825e4ef92e6c93 |
completed | May 19, 2026, 3:05 a.m. |
| NEDg | Description generation | batch_6a0bd780bf048190be000d6b01b7a69e |
completed | May 19, 2026, 3:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bd80b76248190b3878e6f6992db25 |
completed | May 19, 2026, 3:24 a.m. |
Created at: April 17, 2026, 3:50 p.m.