Triple
T1137800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juicy |
E23178
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Samuel Barnes
Samuel Barnes is an author known for his work with the Juicy brand.
|
E229383
|
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: Samuel Barnes | Statement: [Juicy, writer, Samuel Barnes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samuel Barnes Context triple: [Juicy, writer, Samuel Barnes]
-
A.
Samuel Ellis
Samuel Ellis was the landowner after whom Ellis Island in New York Harbor was named.
-
B.
George Barnes
George Barnes was an American cinematographer renowned for his work on numerous classic Hollywood films from the silent era through the 1950s, earning multiple Academy Award nominations and one win.
-
C.
Samuel Allison
Samuel Allison was an American physicist known for his work on nuclear physics and his leadership role in the Manhattan Project at the University of Chicago’s Metallurgical Laboratory.
-
D.
Samuel Gray
Samuel Gray was one of the colonial civilians killed by British soldiers during the 1770 Boston Massacre, an event that helped fuel American revolutionary sentiment.
-
E.
Samuel Cooper
Samuel Cooper was a senior Confederate general who served as the highest-ranking officer and Adjutant and Inspector General of the Confederate States Army during the American Civil War.
- 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: Samuel Barnes Triple: [Juicy, writer, Samuel Barnes]
Generated description
Samuel Barnes is an author known for his work with the Juicy brand.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Samuel Barnes Target entity description: Samuel Barnes is an author known for his work with the Juicy brand.
-
A.
Samuel Ellis
Samuel Ellis was the landowner after whom Ellis Island in New York Harbor was named.
-
B.
George Barnes
George Barnes was an American cinematographer renowned for his work on numerous classic Hollywood films from the silent era through the 1950s, earning multiple Academy Award nominations and one win.
-
C.
Samuel Allison
Samuel Allison was an American physicist known for his work on nuclear physics and his leadership role in the Manhattan Project at the University of Chicago’s Metallurgical Laboratory.
-
D.
Samuel Gray
Samuel Gray was one of the colonial civilians killed by British soldiers during the 1770 Boston Massacre, an event that helped fuel American revolutionary sentiment.
-
E.
Samuel Cooper
Samuel Cooper was a senior Confederate general who served as the highest-ranking officer and Adjutant and Inspector General of the Confederate States Army during the American Civil War.
- 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_69a493ec75988190b63a11bafaec29b4 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc25dda481909a26d726fdbdbb50 |
completed | March 1, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1fa73fe8819097bcd6c07793bc52 |
completed | March 9, 2026, 1:17 a.m. |
| NEDg | Description generation | batch_69ae20efd4dc8190addf69c80a33d247 |
completed | March 9, 2026, 1:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae21b4ce248190bbd7542592db0ec4 |
completed | March 9, 2026, 1:26 a.m. |
Created at: March 1, 2026, 7:44 p.m.