Triple
T20110020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nell Carter |
E490302
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Guy Garner
Guy Garner is best known as the husband of the late American singer and actress Nell Carter.
|
E1412526
|
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: Guy Garner | Statement: [Nell Carter, spouse, Guy Garner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guy Garner Context triple: [Nell Carter, spouse, Guy Garner]
-
A.
Hugh Garner
Hugh Garner was a Canadian author best known for his socially conscious novels and short stories depicting working-class life in Toronto.
-
B.
Guy Darrin Gardner
Guy Darrin Gardner is a fictional DC Comics superhero best known as the brash and hot-headed Green Lantern of Earth.
-
C.
Sid Garner
Sid Garner is a fictional character from "The Hangover" film series, known as the wealthy and often exasperated father-in-law of Doug Billings.
-
D.
Gene Garrick
Gene Garrick was an actor who appeared in classic Hollywood films, including the 1942 crime drama "The Glass Key."
-
E.
Mr. Garner
Mr. Garner is the white slave owner of Sweet Home plantation in Toni Morrison’s novel "Beloved," known for his comparatively lenient yet still oppressive treatment of the enslaved people there.
- 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: Guy Garner Triple: [Nell Carter, spouse, Guy Garner]
Generated description
Guy Garner is best known as the husband of the late American singer and actress Nell Carter.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Guy Garner Target entity description: Guy Garner is best known as the husband of the late American singer and actress Nell Carter.
-
A.
Hugh Garner
Hugh Garner was a Canadian author best known for his socially conscious novels and short stories depicting working-class life in Toronto.
-
B.
Guy Darrin Gardner
Guy Darrin Gardner is a fictional DC Comics superhero best known as the brash and hot-headed Green Lantern of Earth.
-
C.
Sid Garner
Sid Garner is a fictional character from "The Hangover" film series, known as the wealthy and often exasperated father-in-law of Doug Billings.
-
D.
Gene Garrick
Gene Garrick was an actor who appeared in classic Hollywood films, including the 1942 crime drama "The Glass Key."
-
E.
Mr. Garner
Mr. Garner is the white slave owner of Sweet Home plantation in Toni Morrison’s novel "Beloved," known for his comparatively lenient yet still oppressive treatment of the enslaved people there.
- 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e666e0180c8190adeead0e60a099b0 |
completed | April 20, 2026, 5:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a082dd11f948190953f24ca5db4bfa5 |
completed | May 16, 2026, 8:41 a.m. |
| NEDg | Description generation | batch_6a082ec3d1348190baa740b2c28af65c |
completed | May 16, 2026, 8:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a082f9cf5908190b9b766c70164ee75 |
completed | May 16, 2026, 8:49 a.m. |
Created at: April 11, 2026, 11:28 p.m.