Triple
T8731924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howell |
E207275
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Megan Howell
Megan Howell is a person notable enough to be specifically referenced by name, though no widely recognized public information about her is provided in this context.
|
E772343
|
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 Howell | Statement: [Howell, hasNotableBearer, Megan Howell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megan Howell Context triple: [Howell, hasNotableBearer, Megan Howell]
-
A.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
-
B.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
-
C.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
-
D.
Megan Hunt
Megan Hunt is the brilliant but emotionally complex medical examiner protagonist of the television series "Body of Proof."
-
E.
Megan Burns
Megan Burns is a British actress best known for her role as Hannah in the post-apocalyptic horror film "28 Days Later."
- 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 Howell Triple: [Howell, hasNotableBearer, Megan Howell]
Generated description
Megan Howell is a person notable enough to be specifically referenced by name, though no widely recognized public information about her is provided in this context.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Megan Howell Target entity description: Megan Howell is a person notable enough to be specifically referenced by name, though no widely recognized public information about her is provided in this context.
-
A.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
-
B.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
-
C.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
-
D.
Megan Hunt
Megan Hunt is the brilliant but emotionally complex medical examiner protagonist of the television series "Body of Proof."
-
E.
Megan Burns
Megan Burns is a British actress best known for her role as Hannah in the post-apocalyptic horror film "28 Days Later."
- 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_69ca8358e4008190898471a59b96c301 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d27efb88190b42d5bc9774d9c63 |
completed | March 31, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdb7875448190a2478cbb623c31e6 |
completed | April 3, 2026, 3:23 p.m. |
| NEDg | Description generation | batch_69cfdc384884819083334642471c274a |
completed | April 3, 2026, 3:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfdc99a59c8190b509f66fa656bd77 |
completed | April 3, 2026, 3:28 p.m. |
Created at: March 30, 2026, 6:37 p.m.