Triple
T10717295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel Dixon |
E252710
|
entity |
| Predicate | notableWorkWith |
P26239
|
FINISHED |
| Object |
Meg Myers
Meg Myers is an American singer-songwriter known for her intense, emotionally charged alternative rock and dark pop music.
|
E887145
|
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: Meg Myers | Statement: [Samuel Dixon, notableWorkWith, Meg Myers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meg Myers Context triple: [Samuel Dixon, notableWorkWith, Meg Myers]
-
A.
Lisa Meyers
Lisa Meyers is an American attorney best known as the longtime wife of singer-songwriter and actor Kris Kristofferson.
-
B.
Lori McCreary
Lori McCreary is an American film and television producer, co-founder of Revelations Entertainment, and longtime producing partner of actor Morgan Freeman.
-
C.
Melissa Mathison
Melissa Mathison was an American screenwriter best known for writing the screenplay for Steven Spielberg’s film "E.T. the Extra-Terrestrial."
-
D.
Meg Haston
Meg Haston is an American author best known for her middle-grade and young adult novels, including the book that inspired the Nickelodeon television series "How to Rock."
-
E.
Nessa Jenkins
Nessa Jenkins is a deadpan, no-nonsense Welsh character from the British sitcom "Gavin & Stacey," known for her eccentric stories and iconic catchphrases.
- 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: Meg Myers Triple: [Samuel Dixon, notableWorkWith, Meg Myers]
Generated description
Meg Myers is an American singer-songwriter known for her intense, emotionally charged alternative rock and dark pop music.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Meg Myers Target entity description: Meg Myers is an American singer-songwriter known for her intense, emotionally charged alternative rock and dark pop music.
-
A.
Lisa Meyers
Lisa Meyers is an American attorney best known as the longtime wife of singer-songwriter and actor Kris Kristofferson.
-
B.
Lori McCreary
Lori McCreary is an American film and television producer, co-founder of Revelations Entertainment, and longtime producing partner of actor Morgan Freeman.
-
C.
Melissa Mathison
Melissa Mathison was an American screenwriter best known for writing the screenplay for Steven Spielberg’s film "E.T. the Extra-Terrestrial."
-
D.
Meg Haston
Meg Haston is an American author best known for her middle-grade and young adult novels, including the book that inspired the Nickelodeon television series "How to Rock."
-
E.
Nessa Jenkins
Nessa Jenkins is a deadpan, no-nonsense Welsh character from the British sitcom "Gavin & Stacey," known for her eccentric stories and iconic catchphrases.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6ff3566e88190b5c0c6d1b159dd45 |
completed | April 9, 2026, 1:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de847e0ee48190a4c1470fc5296213 |
completed | April 14, 2026, 6:16 p.m. |
| NEDg | Description generation | batch_69de8954500c81909b57c4f8007959aa |
completed | April 14, 2026, 6:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69de8f38e3048190b1acc81bb56fe165 |
completed | April 14, 2026, 7:02 p.m. |
Created at: April 8, 2026, 9:13 p.m.