Triple
T11473760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chopped |
E271973
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object |
Linda Lea
Linda Lea is a television producer best known for creating the popular cooking competition show "Chopped."
|
E972696
|
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: Linda Lea | Statement: [Chopped, creator, Linda Lea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linda Lea Context triple: [Chopped, creator, Linda Lea]
-
A.
Lea Hurst
Lea Hurst is a historic country house in Derbyshire, England, best known as the childhood home of Florence Nightingale.
-
B.
Linda Fennimore
Linda Fennimore is an artist best known for creating the cover art for Stephen King’s horror novel "Pet Sematary."
-
C.
Laura Rister
Laura Rister is a film producer and executive known for her work on independent and prestige projects, including the financial thriller "Margin Call."
-
D.
Audra Lindley
Audra Lindley was an American actress best known for her role as the quirky landlady Helen Roper on the television sitcom "Three's Company" and its spin-off "The Ropers."
-
E.
Linda Nordley
Linda Nordley is a central female character in the 1953 adventure film "Mogambo," portrayed as a refined Englishwoman whose arrival complicates the romantic and emotional dynamics on an African safari.
- 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: Linda Lea Triple: [Chopped, creator, Linda Lea]
Generated description
Linda Lea is a television producer best known for creating the popular cooking competition show "Chopped."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Linda Lea Target entity description: Linda Lea is a television producer best known for creating the popular cooking competition show "Chopped."
-
A.
Lea Hurst
Lea Hurst is a historic country house in Derbyshire, England, best known as the childhood home of Florence Nightingale.
-
B.
Linda Fennimore
Linda Fennimore is an artist best known for creating the cover art for Stephen King’s horror novel "Pet Sematary."
-
C.
Laura Rister
Laura Rister is a film producer and executive known for her work on independent and prestige projects, including the financial thriller "Margin Call."
-
D.
Audra Lindley
Audra Lindley was an American actress best known for her role as the quirky landlady Helen Roper on the television sitcom "Three's Company" and its spin-off "The Ropers."
-
E.
Linda Nordley
Linda Nordley is a central female character in the 1953 adventure film "Mogambo," portrayed as a refined Englishwoman whose arrival complicates the romantic and emotional dynamics on an African safari.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8294b3f388190a587c358313f7260 |
completed | April 9, 2026, 10:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a4f804c81909abf5e9a88da1d91 |
completed | May 2, 2026, 2:29 p.m. |
| NEDg | Description generation | batch_69f61a13fd1481908a06ca65b276e0e1 |
completed | May 2, 2026, 3:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f61ad2bd0c8190ada37bc1f8ae160f |
completed | May 2, 2026, 3:40 p.m. |
Created at: April 8, 2026, 9:35 p.m.