Triple
T34181769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruth Condomine |
E876838
|
entity |
| Predicate | relationshipToElvira |
P205353
|
FINISHED |
| Object | second wife of Elvira’s widower |
—
|
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: second wife of Elvira’s widower | Statement: [Ruth Condomine, relationshipToElvira, second wife of Elvira’s widower]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToElvira Context triple: [Ruth Condomine, relationshipToElvira, second wife of Elvira’s widower]
-
A.
relationshipToEva
Indicates a specified type of personal or social relationship that an entity has with Eva.
-
B.
relationshipToViola
Indicates the specific familial, social, or interpersonal connection that one entity has to the entity named Viola.
-
C.
relationshipToEleanorVance
Indicates the specific nature or type of relationship an entity has with Eleanor Vance.
-
D.
relationshipToVeronika
Indicates the specific type of personal, social, or familial relationship that one entity has to Veronika.
-
E.
relationshipToCelestina
Indicates the specific type of personal, social, or familial relationship that one entity has to Celestina.
- 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_69f349ae640c8190b9cd220b5368d8b6 |
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, 1:54 a.m.