Triple
T7554454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mermaids |
E178623
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Michael Schoeffling
Michael Schoeffling is an American former actor and model best known for his role as Jake Ryan in the 1984 film "Sixteen Candles."
|
E689371
|
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: Michael Schoeffling | Statement: [Mermaids, starring, Michael Schoeffling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Schoeffling Context triple: [Mermaids, starring, Michael Schoeffling]
-
A.
Christian Specht
Christian Specht is a German politician who serves as the mayor of the city of Mannheim.
-
B.
Michael Schiffer
Michael Schiffer is an American screenwriter and playwright best known for scripting films such as "Lean on Me," "Crimson Tide," and "The Peacemaker."
-
C.
Eric Schoffstall
Eric Schoffstall is a software developer best known for creating Gulp, a popular JavaScript-based task runner used in web development build workflows.
-
D.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
-
E.
Ken Schretzmann
Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
- 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: Michael Schoeffling Triple: [Mermaids, starring, Michael Schoeffling]
Generated description
Michael Schoeffling is an American former actor and model best known for his role as Jake Ryan in the 1984 film "Sixteen Candles."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Schoeffling Target entity description: Michael Schoeffling is an American former actor and model best known for his role as Jake Ryan in the 1984 film "Sixteen Candles."
-
A.
Christian Specht
Christian Specht is a German politician who serves as the mayor of the city of Mannheim.
-
B.
Michael Schiffer
Michael Schiffer is an American screenwriter and playwright best known for scripting films such as "Lean on Me," "Crimson Tide," and "The Peacemaker."
-
C.
Eric Schoffstall
Eric Schoffstall is a software developer best known for creating Gulp, a popular JavaScript-based task runner used in web development build workflows.
-
D.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
-
E.
Ken Schretzmann
Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8b990148190b26a3a262cf538b3 |
completed | March 27, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8fa25e4a881909af09d8cbe6852dd |
completed | March 29, 2026, 10:08 a.m. |
| NEDg | Description generation | batch_69c8fc426bf88190a97e55469daa56d6 |
completed | March 29, 2026, 10:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8fc5aca488190b52e8f0d336cda8e |
completed | March 29, 2026, 10:18 a.m. |
Created at: March 27, 2026, 3:49 p.m.