Triple
T19383360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Fang (2018 film) |
E484867
|
entity |
| Predicate | voiceCastMember |
P9616
|
FINISHED |
| Object |
Tom Morton
Tom Morton is a voice actor known for his role in the animated film "White Fang" (2018).
|
E1373205
|
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: Tom Morton | Statement: [White Fang (2018 film), voiceCastMember, Tom Morton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Morton Context triple: [White Fang (2018 film), voiceCastMember, Tom Morton]
-
A.
Rob Morton
Rob Morton is a pseudonym used by American screenwriter Nancy Dowd, known for works such as the film "Slap Shot."
-
B.
Craig Morton
Craig Morton is a former American football quarterback best known for leading both the Dallas Cowboys and Denver Broncos to Super Bowl appearances in the 1970s.
-
C.
Paul Morton
Paul Morton was an American businessman and politician who served as U.S. Secretary of the Navy under President Theodore Roosevelt and was a prominent member of the Morton Salt family.
-
D.
Phil Johnston
Phil Johnston is an American screenwriter and filmmaker known for co-writing animated hits such as Disney's "Zootopia" and "Wreck-It Ralph."
-
E.
Mike Talman
Mike Talman is a con man who becomes entangled in a tense scheme involving a blind woman and hidden heroin in the thriller "Wait Until Dark."
- 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: Tom Morton Triple: [White Fang (2018 film), voiceCastMember, Tom Morton]
Generated description
Tom Morton is a voice actor known for his role in the animated film "White Fang" (2018).
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tom Morton Target entity description: Tom Morton is a voice actor known for his role in the animated film "White Fang" (2018).
-
A.
Rob Morton
Rob Morton is a pseudonym used by American screenwriter Nancy Dowd, known for works such as the film "Slap Shot."
-
B.
Craig Morton
Craig Morton is a former American football quarterback best known for leading both the Dallas Cowboys and Denver Broncos to Super Bowl appearances in the 1970s.
-
C.
Paul Morton
Paul Morton was an American businessman and politician who served as U.S. Secretary of the Navy under President Theodore Roosevelt and was a prominent member of the Morton Salt family.
-
D.
Phil Johnston
Phil Johnston is an American screenwriter and filmmaker known for co-writing animated hits such as Disney's "Zootopia" and "Wreck-It Ralph."
-
E.
Mike Talman
Mike Talman is a con man who becomes entangled in a tense scheme involving a blind woman and hidden heroin in the thriller "Wait Until Dark."
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a614cf88190b561eafaa350ce19 |
completed | April 20, 2026, 12:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a072b7ec8ac8190b9330f239e44f795 |
completed | May 15, 2026, 2:19 p.m. |
| NEDg | Description generation | batch_6a072de198fc8190838ce30942cc46df |
completed | May 15, 2026, 2:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a072e5e62a88190af09d9911a6f1413 |
completed | May 15, 2026, 2:31 p.m. |
Created at: April 10, 2026, 1:35 p.m.