Triple
T1522141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Take |
E32251
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Peter Roeck
Peter Roeck is a film editor known for his work on the movie "The Take."
|
E189351
|
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: Peter Roeck | Statement: [The Take, editedBy, Peter Roeck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Roeck Context triple: [The Take, editedBy, Peter Roeck]
-
A.
Thomas Rongen
Thomas Rongen is a Dutch-American soccer coach and former player known for his extensive coaching career in Major League Soccer and with various U.S. national youth teams.
-
B.
Leo Geurts
Leo Geurts was a Dutch computer scientist known for co-developing the ABC programming language, an influential precursor to Python.
-
C.
Rogier Stoffers
Rogier Stoffers is a Dutch cinematographer known for his work on a range of international films and television productions.
-
D.
Johannes Kleiman
Johannes Kleiman was a Dutch office manager and resistance helper who assisted in hiding Anne Frank and her family during the Nazi occupation of the Netherlands.
-
E.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
- 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: Peter Roeck Triple: [The Take, editedBy, Peter Roeck]
Generated description
Peter Roeck is a film editor known for his work on the movie "The Take."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peter Roeck Target entity description: Peter Roeck is a film editor known for his work on the movie "The Take."
-
A.
Thomas Rongen
Thomas Rongen is a Dutch-American soccer coach and former player known for his extensive coaching career in Major League Soccer and with various U.S. national youth teams.
-
B.
Leo Geurts
Leo Geurts was a Dutch computer scientist known for co-developing the ABC programming language, an influential precursor to Python.
-
C.
Rogier Stoffers
Rogier Stoffers is a Dutch cinematographer known for his work on a range of international films and television productions.
-
D.
Johannes Kleiman
Johannes Kleiman was a Dutch office manager and resistance helper who assisted in hiding Anne Frank and her family during the Nazi occupation of the Netherlands.
-
E.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
- 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a907fe8b0c8190a765afd3a10ee5e0 |
completed | March 5, 2026, 4:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad719b6c988190a525539d1a29d8d4 |
completed | March 8, 2026, 12:54 p.m. |
| NEDg | Description generation | batch_69ad728cb27c8190802b30afc5e259e2 |
completed | March 8, 2026, 12:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad72fa21208190b596bfdfc69043bd |
completed | March 8, 2026, 1 p.m. |
Created at: March 4, 2026, 7:26 p.m.