Triple
T9246802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andrea Wyatt |
E222215
|
entity |
| Predicate | hasRelationshipTypeWithTobyZiegler |
P7844
|
FINISHED |
| Object | former spouse |
—
|
LITERAL 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: former spouse | Statement: [Andrea Wyatt, hasRelationshipTypeWithTobyZiegler, former spouse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithTobyZiegler Context triple: [Andrea Wyatt, hasRelationshipTypeWithTobyZiegler, former spouse]
-
A.
hasRelationshipTypeWithCollinFenwick
Indicates that an entity has a specific type of relationship or connection with Collin Fenwick.
-
B.
hasFamilialTieTo
chosen
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
-
C.
relationshipToGreenBayPackers
Indicates the nature of a person or entity’s connection or association with the Green Bay Packers.
-
D.
hasNotablePersonConnection
Indicates that there exists a significant or noteworthy personal, professional, or historical relationship between the subject and the referenced person.
-
E.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
- F. None of above.
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_69ca841d2b18819089f9faf5b2c2aec0 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd03f181e081908d9ff7dc6f86420e |
completed | April 1, 2026, 11:39 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4765648190aa9445c4a22dc471 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:30 p.m.