Triple
T14001646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kirstie Alley |
E336838
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Parker Stevenson
Parker Stevenson is an American actor best known for his roles in the television series "The Hardy Boys/Nancy Drew Mysteries" and "Baywatch."
|
E1075411
|
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: Parker Stevenson | Statement: [Kirstie Alley, spouse, Parker Stevenson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parker Stevenson Context triple: [Kirstie Alley, spouse, Parker Stevenson]
-
A.
Parker Thomson
Parker Thomson was a prominent Miami attorney and philanthropist known for his significant support of the performing arts.
-
B.
Austin Parker
Austin Parker was the husband of American actress Miriam Hopkins, known primarily in relation to her personal life rather than for a prominent public career of his own.
-
C.
Joel Parker
Joel Parker is a name shared by several notable individuals, including historical American politicians and jurists.
-
D.
Michael Parker
Michael Parker is a film editor best known for his work on the British comedy-drama "Made in Dagenham."
-
E.
Parker Harris
Parker Harris is a technology entrepreneur best known as a co-founder and longtime chief technology leader of the cloud-based software company Salesforce.
- 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: Parker Stevenson Triple: [Kirstie Alley, spouse, Parker Stevenson]
Generated description
Parker Stevenson is an American actor best known for his roles in the television series "The Hardy Boys/Nancy Drew Mysteries" and "Baywatch."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Parker Stevenson Target entity description: Parker Stevenson is an American actor best known for his roles in the television series "The Hardy Boys/Nancy Drew Mysteries" and "Baywatch."
-
A.
Parker Thomson
Parker Thomson was a prominent Miami attorney and philanthropist known for his significant support of the performing arts.
-
B.
Austin Parker
Austin Parker was the husband of American actress Miriam Hopkins, known primarily in relation to her personal life rather than for a prominent public career of his own.
-
C.
Joel Parker
Joel Parker is a name shared by several notable individuals, including historical American politicians and jurists.
-
D.
Michael Parker
Michael Parker is a film editor best known for his work on the British comedy-drama "Made in Dagenham."
-
E.
Parker Harris
Parker Harris is a technology entrepreneur best known as a co-founder and longtime chief technology leader of the cloud-based software company Salesforce.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed06a50819093ddc64f55050689 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc329891c8190b4dcb9913e235a1c |
completed | May 6, 2026, 10:39 p.m. |
| NEDg | Description generation | batch_69fbc5964b7c8190babbb3bd50a1aaec |
completed | May 6, 2026, 10:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fbc912f0e08190be7c4f671b499c57 |
completed | May 6, 2026, 11:04 p.m. |
Created at: April 9, 2026, 10:19 p.m.