Triple
T10323230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suzie Gold |
E242691
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Suzanne Goldish
Suzanne Goldish is a television and film producer, also known professionally as Suzie Gold, recognized for her work behind the scenes in entertainment production.
|
E882346
|
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: Suzanne Goldish | Statement: [Suzie Gold, producer, Suzanne Goldish]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzanne Goldish Context triple: [Suzie Gold, producer, Suzanne Goldish]
-
A.
Suzanne Goldberg
Suzanne Goldberg is a prominent civil rights lawyer and legal scholar known for her work on free speech and equality issues in the United States.
-
B.
Suzanne Goldberg
Suzanne Goldberg is known as the wife of prominent 1960s Free Speech Movement leader Mario Savio.
-
C.
Linda Goldstein
Linda Goldstein is a music producer known for her work on projects such as the album "Simple Pleasures."
-
D.
Jenette Goldstein
Jenette Goldstein is an American actress best known for her tough, memorable supporting roles in science fiction and action films of the 1980s and 1990s.
-
E.
Marion Goldin
Marion Goldin is an American television news producer best known for her long tenure on the CBS newsmagazine "60 Minutes."
- 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: Suzanne Goldish Triple: [Suzie Gold, producer, Suzanne Goldish]
Generated description
Suzanne Goldish is a television and film producer, also known professionally as Suzie Gold, recognized for her work behind the scenes in entertainment production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Suzanne Goldish Target entity description: Suzanne Goldish is a television and film producer, also known professionally as Suzie Gold, recognized for her work behind the scenes in entertainment production.
-
A.
Suzanne Goldberg
Suzanne Goldberg is a prominent civil rights lawyer and legal scholar known for her work on free speech and equality issues in the United States.
-
B.
Suzanne Goldberg
Suzanne Goldberg is known as the wife of prominent 1960s Free Speech Movement leader Mario Savio.
-
C.
Linda Goldstein
Linda Goldstein is a music producer known for her work on projects such as the album "Simple Pleasures."
-
D.
Jenette Goldstein
Jenette Goldstein is an American actress best known for her tough, memorable supporting roles in science fiction and action films of the 1980s and 1990s.
-
E.
Marion Goldin
Marion Goldin is an American television news producer best known for her long tenure on the CBS newsmagazine "60 Minutes."
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d6cdb6cc8190b37ca4494287128b |
completed | April 7, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbd93b506c8190bbff63903770355a |
completed | April 12, 2026, 5:41 p.m. |
| NEDg | Description generation | batch_69dcad07b51081908fd66ee9ff7341f6 |
completed | April 13, 2026, 8:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69dd4386e3308190bb8503ce75fa628f |
completed | April 13, 2026, 7:27 p.m. |
Created at: April 6, 2026, 11:50 a.m.