Triple
T9801674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tim Matheson |
E237850
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Megan Murphy Matheson
Megan Murphy Matheson is an American actress and former spouse of actor Tim Matheson.
|
E821726
|
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: Megan Murphy Matheson | Statement: [Tim Matheson, spouse, Megan Murphy Matheson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megan Murphy Matheson Context triple: [Tim Matheson, spouse, Megan Murphy Matheson]
-
A.
Megan McArthur
Megan McArthur is a NASA astronaut and oceanographer best known for her role as a mission specialist on Space Shuttle missions, including the final Hubble Space Telescope servicing flight.
-
B.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
-
C.
Megan Wallace-Cunningham
Megan Wallace-Cunningham is an art dealer best known as the wife of Scottish-American comedian and former late-night talk show host Craig Ferguson.
-
D.
Michelle Mylett
Michelle Mylett is a Canadian actress best known for playing Katy on the comedy series "Letterkenny."
-
E.
Katherine Murphy
Katherine Murphy is a central character in the romantic comedy film "Just Go with It," where she becomes entangled in a web of lies and pretend relationships that evolve into genuine romance.
- 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: Megan Murphy Matheson Triple: [Tim Matheson, spouse, Megan Murphy Matheson]
Generated description
Megan Murphy Matheson is an American actress and former spouse of actor Tim Matheson.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Megan Murphy Matheson Target entity description: Megan Murphy Matheson is an American actress and former spouse of actor Tim Matheson.
-
A.
Megan McArthur
Megan McArthur is a NASA astronaut and oceanographer best known for her role as a mission specialist on Space Shuttle missions, including the final Hubble Space Telescope servicing flight.
-
B.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
-
C.
Megan Wallace-Cunningham
Megan Wallace-Cunningham is an art dealer best known as the wife of Scottish-American comedian and former late-night talk show host Craig Ferguson.
-
D.
Michelle Mylett
Michelle Mylett is a Canadian actress best known for playing Katy on the comedy series "Letterkenny."
-
E.
Katherine Murphy
Katherine Murphy is a central character in the romantic comedy film "Just Go with It," where she becomes entangled in a web of lies and pretend relationships that evolve into genuine romance.
- 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_69ca84dd4608819097ff4ed00feca280 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda62b41048190bcef70a7591830c6 |
completed | April 1, 2026, 11:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c44edac48190a44fdfb858d0dbba |
completed | April 5, 2026, 2:09 a.m. |
| NEDg | Description generation | batch_69d1c50af000819087d643cc41a6fcc8 |
completed | April 5, 2026, 2:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1c5d39b288190b276371591a86399 |
completed | April 5, 2026, 2:15 a.m. |
Created at: March 30, 2026, 8:29 p.m.