Triple
T12683536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hesher |
E303006
|
entity |
| Predicate | cinematography |
P1953
|
FINISHED |
| Object |
Morgan Susser
Morgan Susser is a cinematographer known for his work on the dark comedy-drama film "Hesher."
|
E997244
|
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: Morgan Susser | Statement: [Hesher, cinematography, Morgan Susser]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Morgan Susser Context triple: [Hesher, cinematography, Morgan Susser]
-
A.
Morgan Hess
Morgan Hess is a central character in the 2002 science fiction thriller film "Signs," portrayed as the young son of former priest Graham Hess.
-
B.
Morgan Hentz
Morgan Hentz is an American volleyball libero best known for her standout collegiate career at Stanford University, where she became one of the top defensive players in the nation.
-
C.
Libby Snyder
Libby Snyder is known as the spouse of American poet James Wright.
-
D.
Ariel Scheinermann
Ariel Scheinermann, better known as Ariel Sharon, was an Israeli general and politician who served as the 11th Prime Minister of Israel and played a pivotal role in the country’s military and political history.
-
E.
Lucy Siegle
Lucy Siegle is a British journalist, author, and environmental campaigner known for her work on ethical fashion, sustainability, and consumer responsibility.
- 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: Morgan Susser Triple: [Hesher, cinematography, Morgan Susser]
Generated description
Morgan Susser is a cinematographer known for his work on the dark comedy-drama film "Hesher."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Morgan Susser Target entity description: Morgan Susser is a cinematographer known for his work on the dark comedy-drama film "Hesher."
-
A.
Morgan Hess
Morgan Hess is a central character in the 2002 science fiction thriller film "Signs," portrayed as the young son of former priest Graham Hess.
-
B.
Morgan Hentz
Morgan Hentz is an American volleyball libero best known for her standout collegiate career at Stanford University, where she became one of the top defensive players in the nation.
-
C.
Libby Snyder
Libby Snyder is known as the spouse of American poet James Wright.
-
D.
Ariel Scheinermann
Ariel Scheinermann, better known as Ariel Sharon, was an Israeli general and politician who served as the 11th Prime Minister of Israel and played a pivotal role in the country’s military and political history.
-
E.
Lucy Siegle
Lucy Siegle is a British journalist, author, and environmental campaigner known for her work on ethical fashion, sustainability, and consumer responsibility.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961d68358819095bdaab8adf1dcf0 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671a733a48190b55d296573c86eaf |
completed | May 2, 2026, 9:50 p.m. |
| NEDg | Description generation | batch_69f67285019c8190be831d3f72cf121f |
completed | May 2, 2026, 9:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f67323a724819092425cdb3a070b96 |
completed | May 2, 2026, 9:56 p.m. |
Created at: April 9, 2026, 5:21 p.m.