Triple
T29491252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pete Krumbein |
E748084
|
entity |
| Predicate | relationshipToZeena Krumbein |
P203238
|
FINISHED |
| Object | husband |
—
|
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: husband | Statement: [Pete Krumbein, relationshipToZeena Krumbein, husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToZeena Krumbein Context triple: [Pete Krumbein, relationshipToZeena Krumbein, husband]
-
A.
relationshipToJoannaKramer
Indicates the specific familial, social, or interpersonal connection that one entity has to Joanna Kramer.
-
B.
relationshipTypeWithStephanie Ramzinski
Indicates the specific nature or category of relationship that an entity has with Stephanie Ramzinski.
-
C.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
-
D.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
E.
relationshipToFranKubelik
Indicates the specific type of personal or social relationship an entity has with Fran Kubelik.
- F. None of above. chosen
Provenance (4 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_69f0bd448c6881908aa6b475cefd5ddc |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_6a01416f8a8c8190abc12f68bd23dcc3 |
completed | May 11, 2026, 2:39 a.m. |
| PD | Predicate disambiguation | batch_6a013f3bc2e48190a51ec2707bbdd474 |
completed | May 11, 2026, 2:30 a.m. |
| PDg | Predicate description generation | batch_6a01416eb9588190ad74541d0640a4ba |
completed | May 11, 2026, 2:39 a.m. |
Created at: April 28, 2026, 4:14 p.m.