Triple
T10465591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Fidelity |
E246785
|
entity |
| Predicate | lyricist |
P1360
|
FINISHED |
| Object |
Amanda Green
Amanda Green is an American lyricist and composer known for her work on Broadway musicals and collaborations with prominent theater artists.
|
E901785
|
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: Amanda Green | Statement: [High Fidelity, lyricist, Amanda Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanda Green Context triple: [High Fidelity, lyricist, Amanda Green]
-
A.
Amanda Robinson
Amanda Robinson is the spouse of Jason Robinson.
-
B.
Lindsay Greenbush
Lindsay Greenbush is an American former child actress best known for playing Carrie Ingalls on the television series "Little House on the Prairie" alongside her twin sister Sidney.
-
C.
Lauren Greene
Lauren Greene is the daughter of American politician and U.S. Representative Marjorie Taylor Greene.
-
D.
Amanda Clayton
Amanda Clayton is an American actress best known for her role in the crime drama television series "City on a Hill."
-
E.
Amanda Kelly
Amanda Kelly is a technology entrepreneur best known as a co-founder of Streamlit, an open-source framework for building data and machine learning web apps in Python.
- 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: Amanda Green Triple: [High Fidelity, lyricist, Amanda Green]
Generated description
Amanda Green is an American lyricist and composer known for her work on Broadway musicals and collaborations with prominent theater artists.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amanda Green Target entity description: Amanda Green is an American lyricist and composer known for her work on Broadway musicals and collaborations with prominent theater artists.
-
A.
Amanda Robinson
Amanda Robinson is the spouse of Jason Robinson.
-
B.
Lindsay Greenbush
Lindsay Greenbush is an American former child actress best known for playing Carrie Ingalls on the television series "Little House on the Prairie" alongside her twin sister Sidney.
-
C.
Lauren Greene
Lauren Greene is the daughter of American politician and U.S. Representative Marjorie Taylor Greene.
-
D.
Amanda Clayton
Amanda Clayton is an American actress best known for her role in the crime drama television series "City on a Hill."
-
E.
Amanda Kelly
Amanda Kelly is a technology entrepreneur best known as a co-founder of Streamlit, an open-source framework for building data and machine learning web apps in Python.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5092d6d408190b6bda4d7ced4601e |
completed | April 7, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3a8e98cac8190873af1a2cdb5c5a9 |
completed | April 18, 2026, 3:53 p.m. |
| NEDg | Description generation | batch_69e3abe492388190a2f5752f6bad1220 |
completed | April 18, 2026, 4:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3b1efe4a88190884eb5186954cf39 |
completed | April 18, 2026, 4:31 p.m. |
Created at: April 6, 2026, 12:19 p.m.