Triple
T22198258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bull |
E548603
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Marissa Morgan
Marissa Morgan is a key character on the TV series "Bull," serving as Dr. Jason Bull’s savvy second-in-command and trial strategy expert.
|
E1567812
|
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: Marissa Morgan | Statement: [Bull, character, Marissa Morgan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marissa Morgan Context triple: [Bull, character, Marissa Morgan]
-
A.
Jennifer Morgan
Jennifer Morgan is a central character in the television sitcom "Rules of Engagement," known for navigating the ups and downs of a long-term relationship and engagement with her fiancé Adam.
-
B.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
-
C.
Marissa Tasker
Marissa Tasker is a fictional character from the soap opera "All My Children," known for her complex family ties and dramatic storylines in Pine Valley.
-
D.
Emily Morgan
Emily Morgan is the daughter of Gretchen Morgan.
-
E.
Marissa Wilson
Marissa Wilson is the retired spy-turned-mother protagonist in the family action film "Spy Kids: All the Time in the World."
- 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: Marissa Morgan Triple: [Bull, character, Marissa Morgan]
Generated description
Marissa Morgan is a key character on the TV series "Bull," serving as Dr. Jason Bull’s savvy second-in-command and trial strategy expert.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marissa Morgan Target entity description: Marissa Morgan is a key character on the TV series "Bull," serving as Dr. Jason Bull’s savvy second-in-command and trial strategy expert.
-
A.
Jennifer Morgan
Jennifer Morgan is a central character in the television sitcom "Rules of Engagement," known for navigating the ups and downs of a long-term relationship and engagement with her fiancé Adam.
-
B.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
-
C.
Marissa Tasker
Marissa Tasker is a fictional character from the soap opera "All My Children," known for her complex family ties and dramatic storylines in Pine Valley.
-
D.
Emily Morgan
Emily Morgan is the daughter of Gretchen Morgan.
-
E.
Marissa Wilson
Marissa Wilson is the retired spy-turned-mother protagonist in the family action film "Spy Kids: All the Time in the World."
- 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_69e11e3ecc7c8190b5f94cd8f42e9d37 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12ae8c74c819080c7f9e8383ecaa7 |
completed | April 28, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c0abd066081909ce2709cd85a1551 |
completed | May 19, 2026, 7:01 a.m. |
| NEDg | Description generation | batch_6a0c0b70986c81909e1db90201b55f9d |
completed | May 19, 2026, 7:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c0c368c1c81909075c3d9802cfa2f |
completed | May 19, 2026, 7:07 a.m. |
Created at: April 16, 2026, 8:36 p.m.