Triple
T33181244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diane Sterling |
E849332
|
entity |
| Predicate | relationshipTypeWithGibbs |
P142888
|
FINISHED |
| Object | divorced |
—
|
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: divorced | Statement: [Diane Sterling, relationshipTypeWithGibbs, divorced]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithGibbs Context triple: [Diane Sterling, relationshipTypeWithGibbs, divorced]
-
A.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
relationshipToBond
Indicates the specific type of personal, familial, or professional relationship an entity has to the person named Bond.
-
C.
relationshipTypeWithSassi
chosen
Indicates the specific type or nature of the relationship that an entity has with Sassi.
-
D.
relatedType
Indicates that one entity is connected to another through a specified type or category of relationship.
-
E.
unitRelation
Indicates a relationship between units, such as how one unit is associated with, derived from, or converted to another.
- 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_69f3495d06508190b0b7729982982cea |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:29 a.m.