Triple
T16247637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bus Stop (play) |
E394414
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Will Masters
Will Masters is the tough yet vulnerable cowboy and central male protagonist in William Inge’s play "Bus Stop."
|
E1201790
|
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: Will Masters | Statement: [Bus Stop (play), hasMainCharacter, Will Masters]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Will Masters Context triple: [Bus Stop (play), hasMainCharacter, Will Masters]
-
A.
Ken Masters
Ken Masters is a fiery, blonde American martial artist from the Street Fighter video game series, known for his aggressive fighting style and friendly rivalry with Ryu.
-
B.
Ray Merrimen
Ray Merrimen is a disciplined, battle-hardened ex-Marine and mastermind leader of a crew of professional bank robbers in the crime film "Den of Thieves."
-
C.
Phil Mills
Phil Mills is a Welsh rally co-driver best known for partnering with Petter Solberg to win the 2003 World Rally Championship with the Subaru World Rally Team.
-
D.
Ray Wright
Ray Wright is a screenwriter known for his work on the film "Case 39" and other genre-focused screenplays.
-
E.
Warren Brown
Warren Brown is a British actor best known for his role as DS Justin Ripley in the crime drama series "Luther."
- 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: Will Masters Triple: [Bus Stop (play), hasMainCharacter, Will Masters]
Generated description
Will Masters is the tough yet vulnerable cowboy and central male protagonist in William Inge’s play "Bus Stop."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Will Masters Target entity description: Will Masters is the tough yet vulnerable cowboy and central male protagonist in William Inge’s play "Bus Stop."
-
A.
Ken Masters
Ken Masters is a fiery, blonde American martial artist from the Street Fighter video game series, known for his aggressive fighting style and friendly rivalry with Ryu.
-
B.
Ray Merrimen
Ray Merrimen is a disciplined, battle-hardened ex-Marine and mastermind leader of a crew of professional bank robbers in the crime film "Den of Thieves."
-
C.
Phil Mills
Phil Mills is a Welsh rally co-driver best known for partnering with Petter Solberg to win the 2003 World Rally Championship with the Subaru World Rally Team.
-
D.
Ray Wright
Ray Wright is a screenwriter known for his work on the film "Case 39" and other genre-focused screenplays.
-
E.
Warren Brown
Warren Brown is a British actor best known for his role as DS Justin Ripley in the crime drama series "Luther."
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245942460819080897afad0d2fe09 |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000ee3bbc48190a56ce2807a9510f0 |
completed | May 10, 2026, 4:51 a.m. |
| NEDg | Description generation | batch_6a000f9aecec819087cecb1edad6b710 |
completed | May 10, 2026, 4:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0010462ba881909666051b2fc38d43 |
completed | May 10, 2026, 4:57 a.m. |
Created at: April 10, 2026, 5:04 a.m.