Triple
T14958367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vicki Lawrence |
E372992
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Al Schultz |
E841917
|
NE FINISHED |
How this triple was built (2 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: Al Schultz | Statement: [Vicki Lawrence, spouse, Al Schultz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Al Schultz Context triple: [Vicki Lawrence, spouse, Al Schultz]
-
A.
Al Schultz
chosen
Al Schultz is a notable individual who shares the surname Schultz and has achieved enough recognition to be specifically distinguished by name.
-
B.
William Schultz
William Schultz is a relatively common personal name shared by multiple individuals across various professions and public roles.
-
C.
Michael Schultz
Michael Schultz is an American film and television director best known for his influential work on 1970s comedies and dramas, including the cult classic "Car Wash."
-
D.
Ron Schultz
Ron Schultz is a notable individual recognized for achievements significant enough to be distinctly associated with the surname Schultz.
-
E.
Kevin M. Schultz
Kevin M. Schultz is an American historian and author known for his work on modern American history, religion, and pluralism, and for writing widely used college history textbooks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cd85bc81909040b7ff78f62554 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e9e74fc8190bdd10a25c39829f3 |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 2:40 a.m.