Triple
T14975934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pete Ricketts |
E373447
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Susanne Shore |
E373447
|
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: Susanne Shore | Statement: [Pete Ricketts, spouse, Susanne Shore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Susanne Shore Context triple: [Pete Ricketts, spouse, Susanne Shore]
-
A.
Susanne Shore
chosen
Susanne Shore is an American public figure known primarily as the wife of former Nebraska governor and U.S. Senator Pete Ricketts.
-
B.
Patricia Russo
Patricia Russo is an American business executive best known for serving as CEO of Lucent Technologies and later Alcatel-Lucent.
-
C.
Ann Shoemaker
Ann Shoemaker was an American character actress known for her numerous supporting roles in stage and film during the early to mid-20th century.
-
D.
Gail Berke
Gail Berke is a central protagonist in the adventure film "The Deep," known for becoming entangled in a dangerous underwater treasure hunt.
-
E.
Diane Sherry
Diane Sherry is an actress best known for playing Lana Lang in the 1978 superhero film "Superman."
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e8733081908e06b53746eb6eb6 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8beac05c8190bf19ec8bd1eab2d8 |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:51 a.m.