Triple
T15253873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 28 Days Later |
E364589
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Megan Burns |
—
|
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: Megan Burns | Statement: [28 Days Later, castMember, Megan Burns]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megan Burns Context triple: [28 Days Later, castMember, Megan Burns]
-
A.
Megan Burns
chosen
Megan Burns is a British actress best known for her role as Hannah in the post-apocalyptic horror film "28 Days Later."
-
B.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
-
C.
Megan Terry
Megan Terry is an influential American playwright and pioneer of feminist and experimental theatre, best known for works like "Viet Rock."
-
D.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
-
E.
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.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007f8cb308190933c4478aa096e24 |
completed | April 15, 2026, 9:49 p.m. |
Created at: April 10, 2026, 3:13 a.m.