Triple
T6103609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julia Roberts |
E136062
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Daniel Moder
Daniel Moder is an American cinematographer best known for his work on films such as "Secret in Their Eyes" and "The Mexican."
|
E569154
|
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: Daniel Moder | Statement: [Julia Roberts, spouse, Daniel Moder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Moder Context triple: [Julia Roberts, spouse, Daniel Moder]
-
A.
Dan Janvey
Dan Janvey is an American film producer known for his work on acclaimed independent films, including the Academy Award–winning "Nomadland."
-
B.
Joel Stransky
Joel Stransky is a former South African rugby union fly-half best known for kicking the winning drop goal in the 1995 Rugby World Cup final.
-
C.
Jeremy Doner
Jeremy Doner is a screenwriter best known for co-writing the 2022 biographical musical film "Elvis."
-
D.
Calvin Wimmer
Calvin Wimmer is a film editor best known for his work on the science fiction horror movie "The Cloverfield Paradox."
-
E.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
- 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: Daniel Moder Triple: [Julia Roberts, spouse, Daniel Moder]
Generated description
Daniel Moder is an American cinematographer best known for his work on films such as "Secret in Their Eyes" and "The Mexican."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Moder Target entity description: Daniel Moder is an American cinematographer best known for his work on films such as "Secret in Their Eyes" and "The Mexican."
-
A.
Dan Janvey
Dan Janvey is an American film producer known for his work on acclaimed independent films, including the Academy Award–winning "Nomadland."
-
B.
Joel Stransky
Joel Stransky is a former South African rugby union fly-half best known for kicking the winning drop goal in the 1995 Rugby World Cup final.
-
C.
Jeremy Doner
Jeremy Doner is a screenwriter best known for co-writing the 2022 biographical musical film "Elvis."
-
D.
Calvin Wimmer
Calvin Wimmer is a film editor best known for his work on the science fiction horror movie "The Cloverfield Paradox."
-
E.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
- 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_69c0087dee9881909e3655be88208c01 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05b3dbc6c8190b9e3d81e6ca9eeb8 |
completed | March 22, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c12553f1d4819096de40514ef4d2cb |
completed | March 23, 2026, 11:34 a.m. |
| NEDg | Description generation | batch_69c125d888cc819092b765d47f1d9f9f |
completed | March 23, 2026, 11:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c126f308988190ab6cb6c79ea12877 |
completed | March 23, 2026, 11:41 a.m. |
Created at: March 22, 2026, 4:13 p.m.