Triple
T8507032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bounce Back |
E201359
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Amaire Johnson
Amaire Johnson is a writer known for contributing to the work "Bounce Back."
|
E749822
|
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: Amaire Johnson | Statement: [Bounce Back, writer, Amaire Johnson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amaire Johnson Context triple: [Bounce Back, writer, Amaire Johnson]
-
A.
Francesca Johnson
Francesca Johnson is the introspective Italian-American farm wife whose brief, life-altering love affair lies at the emotional core of the romantic drama "The Bridges of Madison County."
-
B.
Evangeline Johnson
Evangeline Johnson was the wife of American film and television actor Van Johnson.
-
C.
Evangeline Johnson
Evangeline Johnson was a member of the prominent Johnson family associated with the founding of Johnson & Johnson.
-
D.
Amara Miller
Amara Miller is an American actress best known for her breakout role as the younger daughter of George Clooney's character in the acclaimed film "The Descendants."
-
E.
Louise Johnson
Louise Johnson was a pioneering British biochemist and crystallographer renowned for her groundbreaking work on protein structure and enzyme mechanisms.
- 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: Amaire Johnson Triple: [Bounce Back, writer, Amaire Johnson]
Generated description
Amaire Johnson is a writer known for contributing to the work "Bounce Back."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amaire Johnson Target entity description: Amaire Johnson is a writer known for contributing to the work "Bounce Back."
-
A.
Francesca Johnson
Francesca Johnson is the introspective Italian-American farm wife whose brief, life-altering love affair lies at the emotional core of the romantic drama "The Bridges of Madison County."
-
B.
Evangeline Johnson
Evangeline Johnson was the wife of American film and television actor Van Johnson.
-
C.
Evangeline Johnson
Evangeline Johnson was a member of the prominent Johnson family associated with the founding of Johnson & Johnson.
-
D.
Amara Miller
Amara Miller is an American actress best known for her breakout role as the younger daughter of George Clooney's character in the acclaimed film "The Descendants."
-
E.
Louise Johnson
Louise Johnson was a pioneering British biochemist and crystallographer renowned for her groundbreaking work on protein structure and enzyme mechanisms.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe5de18448190a695eec609b34e1a |
completed | March 31, 2026, 3:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cecc53555c8190b910b3220bd701b4 |
completed | April 2, 2026, 8:06 p.m. |
| NEDg | Description generation | batch_69ced09be2b08190bcc14c9ff4403fdd |
completed | April 2, 2026, 8:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ced0f1df6c819095322fbe3c8a6f48 |
completed | April 2, 2026, 8:26 p.m. |
Created at: March 30, 2026, 6:14 p.m.