Triple
T13344340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dana Elcar |
E317909
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Thelma M. Garcia
Thelma M. Garcia is best known as the former spouse of American character actor Dana Elcar, recognized for his role on the television series "MacGyver."
|
E1045954
|
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: Thelma M. Garcia | Statement: [Dana Elcar, spouse, Thelma M. Garcia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thelma M. Garcia Context triple: [Dana Elcar, spouse, Thelma M. Garcia]
-
A.
Mildred García
Mildred García is an American academic leader and administrator who serves as chancellor of the California State University system.
-
B.
Bertha Cuellar González
Bertha Cuellar González was the wife of longtime U.S. Congressman Henry B. González and a member of a prominent Mexican American political family in Texas.
-
C.
Marjorie Velázquez
Marjorie Velázquez is a New York City politician who serves on the City Council representing parts of the Bronx.
-
D.
Norma Heyman
Norma Heyman is a British film producer known for her work on acclaimed films such as "Dangerous Liaisons" and "Mrs Henderson Presents."
-
E.
Jane T. Castro
Jane T. Castro is a Filipino politician who serves as the elected representative of the municipality of Dumalag.
- 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: Thelma M. Garcia Triple: [Dana Elcar, spouse, Thelma M. Garcia]
Generated description
Thelma M. Garcia is best known as the former spouse of American character actor Dana Elcar, recognized for his role on the television series "MacGyver."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thelma M. Garcia Target entity description: Thelma M. Garcia is best known as the former spouse of American character actor Dana Elcar, recognized for his role on the television series "MacGyver."
-
A.
Mildred García
Mildred García is an American academic leader and administrator who serves as chancellor of the California State University system.
-
B.
Bertha Cuellar González
Bertha Cuellar González was the wife of longtime U.S. Congressman Henry B. González and a member of a prominent Mexican American political family in Texas.
-
C.
Marjorie Velázquez
Marjorie Velázquez is a New York City politician who serves on the City Council representing parts of the Bronx.
-
D.
Norma Heyman
Norma Heyman is a British film producer known for her work on acclaimed films such as "Dangerous Liaisons" and "Mrs Henderson Presents."
-
E.
Jane T. Castro
Jane T. Castro is a Filipino politician who serves as the elected representative of the municipality of Dumalag.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e8839b48190b164414b418e756c |
completed | April 11, 2026, 1:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f754718a388190b4b85151a4694435 |
completed | May 3, 2026, 1:58 p.m. |
| NEDg | Description generation | batch_69f7571dc684819082395d18970c57bf |
completed | May 3, 2026, 2:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f75787f2348190b71cc679e1c6962d |
completed | May 3, 2026, 2:11 p.m. |
Created at: April 9, 2026, 9:31 p.m.