Triple
T34083644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Willa Ferreyra |
E874116
|
entity |
| Predicate | relationshipStatusWithConnorRoy |
P205309
|
FINISHED |
| Object | married |
—
|
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: married | Statement: [Willa Ferreyra, relationshipStatusWithConnorRoy, married]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipStatusWithConnorRoy Context triple: [Willa Ferreyra, relationshipStatusWithConnorRoy, married]
-
A.
relationshipToConradJarrett
Indicates the specific type of personal or emotional relationship an entity has with Conrad Jarrett.
-
B.
relationshipToLloyd
Indicates the specific type of personal or social relationship an entity has with Lloyd.
-
C.
relationshipStatusWithBrad
Indicates the type or state of the relationship that an entity currently has with Brad.
-
D.
relationshipToRyanBingham
Indicates the specific type of personal or social relationship an entity has with Ryan Bingham.
-
E.
relationshipStatusWithBrian
Indicates the current nature or state of an entity’s personal relationship with Brian.
- 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_69f349a61d448190b74642f325d3eb7a |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f963908190846d232f386fd98f |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:52 a.m.