Triple
T16247639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bus Stop (play) |
E394414
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Carl
Carl is the central character in the play "Bus Stop," around whom much of the story’s interpersonal drama and development revolves.
|
E1201791
|
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: Carl | Statement: [Bus Stop (play), hasMainCharacter, Carl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carl Context triple: [Bus Stop (play), hasMainCharacter, Carl]
-
A.
Carl
Carl is the given name of Carl Sagan, the renowned American astronomer, science communicator, and author.
-
B.
Carl
Carl is the given name of the influential American microbiologist Carl Woese, known for defining the Archaea domain of life.
-
C.
Carl
Carl is the given name of Carl Schorske, a prominent American cultural and intellectual historian known for his influential work on fin-de-siècle Vienna.
-
D.
Carl
Carl is the given name of Carl von Martius, a notable 19th-century German botanist and explorer known for his extensive studies of Brazilian flora.
-
E.
Carl
Carl is the given name of the German Romantic composer and conductor Carl Maria von Weber, known for works such as the opera "Der Freischütz."
- 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: Carl Triple: [Bus Stop (play), hasMainCharacter, Carl]
Generated description
Carl is the central character in the play "Bus Stop," around whom much of the story’s interpersonal drama and development revolves.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Carl Target entity description: Carl is the central character in the play "Bus Stop," around whom much of the story’s interpersonal drama and development revolves.
-
A.
Carl
Carl is a supporting character in the classic film "Casablanca," working at Rick Blaine's nightclub, Rick's Café Américain.
-
B.
Carl
Carl is the abusive father of Claireece "Precious" Jones in the novel and film "Precious," central to the story's depiction of trauma and hardship.
-
C.
Carl
Carl is the protagonist of the psychological horror video game "The Coma," navigating a nightmarish version of his school while uncovering dark secrets.
-
D.
Carl
Carl is a central character in the British comedy film "The Boat That Rocked," portrayed as a young man who joins a pirate radio ship in the 1960s and comes of age amid its rebellious DJs and rock music culture.
-
E.
Carl
Carl is a supporting character in the film "Van Helsing," depicted as a witty and inventive friar who aids the titular monster hunter.
- 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.