Triple
T18383326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theo Rossi |
E446511
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Meghan McDermott |
—
|
NE NERFINISHED |
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: 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.
Meghan McDermott
chosen
Meghan McDermott is an American public relations and communications professional best known for her marriage to actor Theo Rossi.
-
B.
Megan McClure
Megan McClure is an American volleyball player best known as a standout outside hitter for Stanford University's powerhouse women's volleyball program.
-
C.
Rebecca McGuinness
Rebecca McGuinness is known as the wife of renowned English motorcycle road racer John McGuinness.
-
D.
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.
-
E.
Erin McDermott
Erin McDermott is a collegiate sports administrator best known as the athletic director at Harvard University.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e5179c931c8190b1c7c8284f42f7b7 |
completed | April 19, 2026, 5:57 p.m. |
Created at: April 10, 2026, 10:45 a.m.