Triple
T4663571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Watch Over Me |
E102790
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Andre Williams
Andre Williams is a character in the film "Watch Over Me," contributing to the drama’s central interpersonal conflicts and emotional tension.
|
E460639
|
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: Andre Williams | Statement: [Watch Over Me, hasCharacter, Andre Williams]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andre Williams Context triple: [Watch Over Me, hasCharacter, Andre Williams]
-
A.
Mike Williams
Mike Williams is a Swedish computer scientist best known as one of the creators of the Erlang programming language.
-
B.
Brick Breeland
Brick Breeland is a fictional small-town doctor and patriarch in the TV series "Hart of Dixie."
-
C.
Maurice Jones-Drew
Maurice Jones-Drew is a former NFL running back, best known for his Pro Bowl career with the Jacksonville Jaguars and his dynamic, compact running style.
-
D.
Steven Jackson
Steven Jackson is a former NFL running back best known for his productive tenure with the St. Louis Rams, where he became the franchise's all-time leading rusher.
-
E.
Earl Williams
Earl Williams is a pivotal accused murderer whose case drives the fast-paced newsroom drama and darkly comic chaos in the play "The Front Page."
- 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: Andre Williams Triple: [Watch Over Me, hasCharacter, Andre Williams]
Generated description
Andre Williams is a character in the film "Watch Over Me," contributing to the drama’s central interpersonal conflicts and emotional tension.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Andre Williams Target entity description: Andre Williams is a character in the film "Watch Over Me," contributing to the drama’s central interpersonal conflicts and emotional tension.
-
A.
Mike Williams
Mike Williams is a Swedish computer scientist best known as one of the creators of the Erlang programming language.
-
B.
Brick Breeland
Brick Breeland is a fictional small-town doctor and patriarch in the TV series "Hart of Dixie."
-
C.
Maurice Jones-Drew
Maurice Jones-Drew is a former NFL running back, best known for his Pro Bowl career with the Jacksonville Jaguars and his dynamic, compact running style.
-
D.
Steven Jackson
Steven Jackson is a former NFL running back best known for his productive tenure with the St. Louis Rams, where he became the franchise's all-time leading rusher.
-
E.
Earl Williams
Earl Williams is a pivotal accused murderer whose case drives the fast-paced newsroom drama and darkly comic chaos in the play "The Front Page."
- 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_69bd43d9cba4819086c1ab1c2d9d2133 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd632d6150819085bab97021c0235a |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be03803a948190b6dc2a03bb9cdc93 |
completed | March 21, 2026, 2:33 a.m. |
| NEDg | Description generation | batch_69be0542daf08190b792855c8129ac50 |
completed | March 21, 2026, 2:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be05c1dcd48190a08a5748e86a5ac8 |
completed | March 21, 2026, 2:43 a.m. |
Created at: March 20, 2026, 1:15 p.m.