Triple
T4626213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Score |
E101102
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Rob Hahn
Rob Hahn is an American cinematographer best known for his work on feature films such as the crime thriller "The Score."
|
E493642
|
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: Rob Hahn | Statement: [The Score, cinematographyBy, Rob Hahn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rob Hahn Context triple: [The Score, cinematographyBy, Rob Hahn]
-
A.
Rick Hahn
Rick Hahn is an American baseball executive best known for serving as the general manager of the Chicago White Sox in Major League Baseball.
-
B.
Doug J. Hannah
Doug J. Hannah is a film editor best known for his work on the science fiction thriller "The Cloverfield Paradox."
-
C.
Robert Hohman
Robert Hohman is an American entrepreneur best known as the co-founder and former CEO of Glassdoor, a popular platform for anonymous employee reviews and salary information.
-
D.
Lew Hahn
Lew Hahn is a recording engineer known for his work on notable music projects such as the song "I'm Every Woman."
-
E.
Ron Hagen
Ron Hagen is a cinematographer best known for his work on the Australian film "Romper Stomper."
- 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: Rob Hahn Triple: [The Score, cinematographyBy, Rob Hahn]
Generated description
Rob Hahn is an American cinematographer best known for his work on feature films such as the crime thriller "The Score."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rob Hahn Target entity description: Rob Hahn is an American cinematographer best known for his work on feature films such as the crime thriller "The Score."
-
A.
Rick Hahn
Rick Hahn is an American baseball executive best known for serving as the general manager of the Chicago White Sox in Major League Baseball.
-
B.
Doug J. Hannah
Doug J. Hannah is a film editor best known for his work on the science fiction thriller "The Cloverfield Paradox."
-
C.
Robert Hohman
Robert Hohman is an American entrepreneur best known as the co-founder and former CEO of Glassdoor, a popular platform for anonymous employee reviews and salary information.
-
D.
Lew Hahn
Lew Hahn is a recording engineer known for his work on notable music projects such as the song "I'm Every Woman."
-
E.
Ron Hagen
Ron Hagen is a cinematographer best known for his work on the Australian film "Romper Stomper."
- 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_69bd43d0497c8190ac23c65c5804846a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a0a7b588190bc6552ee5babb198 |
completed | March 20, 2026, 2:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba45b1a88190bb607182167060af |
completed | March 21, 2026, 3:33 p.m. |
| NEDg | Description generation | batch_69bebbf5c968819093aaca8a09b2ea14 |
completed | March 21, 2026, 3:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bebc8e25e08190bad1791a97b6482d |
completed | March 21, 2026, 3:43 p.m. |
Created at: March 20, 2026, 1:13 p.m.