Triple
T14409678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mask Off |
E357290
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Tommy Butler
Tommy Butler is a writer best known for his work on the television series "Mask Off."
|
E1098073
|
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: Tommy Butler | Statement: [Mask Off, writer, Tommy Butler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tommy Butler Context triple: [Mask Off, writer, Tommy Butler]
-
A.
Joe Butler
Joe Butler is a film editor best known for his work on the animated feature "Ron’s Gone Wrong."
-
B.
Tommy Warrilow
Tommy Warrilow is an English football manager and former player best known for managing non-league clubs, including a spell in charge of Ashford United F.C.
-
C.
Tommy Cowling
Tommy Cowling is a member of the group or team known as The Warriors.
-
D.
Tommy Beresford
Tommy Beresford is a fictional amateur detective and adventurer who, alongside his wife Tuppence, stars in several of Agatha Christie's mystery novels and short stories.
-
E.
Tommy McClelland
Tommy McClelland is a collegiate athletics administrator known for serving as the athletic director at Rice University and previously holding the same role at Louisiana Tech.
- 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: Tommy Butler Triple: [Mask Off, writer, Tommy Butler]
Generated description
Tommy Butler is a writer best known for his work on the television series "Mask Off."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tommy Butler Target entity description: Tommy Butler is a writer best known for his work on the television series "Mask Off."
-
A.
Joe Butler
Joe Butler is a film editor best known for his work on the animated feature "Ron’s Gone Wrong."
-
B.
Tommy Warrilow
Tommy Warrilow is an English football manager and former player best known for managing non-league clubs, including a spell in charge of Ashford United F.C.
-
C.
Tommy Cowling
Tommy Cowling is a member of the group or team known as The Warriors.
-
D.
Tommy Beresford
Tommy Beresford is a fictional amateur detective and adventurer who, alongside his wife Tuppence, stars in several of Agatha Christie's mystery novels and short stories.
-
E.
Tommy McClelland
Tommy McClelland is a collegiate athletics administrator known for serving as the athletic director at Rice University and previously holding the same role at Louisiana Tech.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90c9b3448190aec1608836a5e913 |
completed | April 14, 2026, 7:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd55269d8c81909592277741a93db6 |
completed | May 8, 2026, 3:14 a.m. |
| NEDg | Description generation | batch_69fd58216a8c8190b1fffcb670f15e16 |
completed | May 8, 2026, 3:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd589144b8819099aadef126b8728f |
completed | May 8, 2026, 3:29 a.m. |
Created at: April 10, 2026, 1:17 a.m.