Triple
T16161662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rules of Engagement |
E392193
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
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.
|
E1397373
|
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: Jennifer Morgan | Statement: [Rules of Engagement, mainCharacter, Jennifer Morgan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Morgan Context triple: [Rules of Engagement, mainCharacter, Jennifer Morgan]
-
A.
Emily Morgan
Emily Morgan is the daughter of Gretchen Morgan.
-
B.
Michelle Morgan
Michelle Morgan is a Canadian actress best known for playing Lou Fleming on the long-running family drama series "Heartland."
-
C.
Michelle Morgan
Michelle Morgan is an actress best known for her voice work on the animated television series "The PJs."
-
D.
Tanya Morgan
Tanya Morgan is an American hip hop group known for their witty, soulful, and concept-driven rap music emerging from the mid-2000s underground scene.
-
E.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
- 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: Jennifer Morgan Triple: [Rules of Engagement, mainCharacter, Jennifer Morgan]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jennifer Morgan Target entity description: 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.
-
A.
Emily Morgan
Emily Morgan is the daughter of Gretchen Morgan.
-
B.
Michelle Morgan
Michelle Morgan is a Canadian actress best known for playing Lou Fleming on the long-running family drama series "Heartland."
-
C.
Michelle Morgan
Michelle Morgan is an actress best known for her voice work on the animated television series "The PJs."
-
D.
Tanya Morgan
Tanya Morgan is an American hip hop group known for their witty, soulful, and concept-driven rap music emerging from the mid-2000s underground scene.
-
E.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21e5ffba88190b9dc7bb9afb6fdf2 |
completed | April 17, 2026, 11:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07ccafa04c8190849980aeda8f3435 |
completed | May 16, 2026, 1:47 a.m. |
| NEDg | Description generation | batch_6a07cfbebe848190a1e7cce64ec19df9 |
completed | May 16, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07d050623c8190bfd3c05193794101 |
completed | May 16, 2026, 2:02 a.m. |
Created at: April 10, 2026, 5:02 a.m.