Triple
T3290614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theo Rossi |
E69090
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Meghan McDermott
Meghan McDermott is an American public relations and communications professional best known for her marriage to actor Theo Rossi.
|
E446511
|
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: Meghan McDermott | Statement: [Theo Rossi, spouse, Meghan McDermott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meghan McDermott Context triple: [Theo Rossi, spouse, Meghan McDermott]
-
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.
Erin McDermott
Erin McDermott is a collegiate sports administrator best known as the athletic director at Harvard University.
-
C.
Megan Walsh
Megan Walsh is the teenage government-trained assassin who goes undercover as a high school student in the action-comedy film "Barely Lethal."
-
D.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
-
E.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
- 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: Meghan McDermott Triple: [Theo Rossi, spouse, Meghan McDermott]
Generated description
Meghan McDermott is an American public relations and communications professional best known for her marriage to actor Theo Rossi.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Meghan McDermott Target entity description: Meghan McDermott is an American public relations and communications professional best known for her marriage to actor Theo Rossi.
-
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.
Erin McDermott
Erin McDermott is a collegiate sports administrator best known as the athletic director at Harvard University.
-
C.
Megan Walsh
Megan Walsh is the teenage government-trained assassin who goes undercover as a high school student in the action-comedy film "Barely Lethal."
-
D.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
-
E.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb05bd6b08190bcb9f0e5da82bc21 |
completed | March 8, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd6786a558819098973b8f10b7e7cb |
completed | March 20, 2026, 3:28 p.m. |
| NEDg | Description generation | batch_69bd6b5a3a488190ba0ff3bfd6277f24 |
completed | March 20, 2026, 3:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bd6c641b488190b8c6860898971aa1 |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 8, 2026, 3:10 p.m.