Triple
T20880717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Byron, 5th Baron Byron |
E514137
|
entity |
| Predicate | kinshipToLordByron |
P142229
|
FINISHED |
| Object | great-uncle |
—
|
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: great-uncle | Statement: [William Byron, 5th Baron Byron, kinshipToLordByron, great-uncle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: kinshipToLordByron Context triple: [William Byron, 5th Baron Byron, kinshipToLordByron, great-uncle]
-
A.
portrayedLordByronAs
Indicates that one entity depicted or represented Lord Byron in some medium or context.
-
B.
relationshipToOnegin
Indicates the specific interpersonal or familial relationship that one entity has to the person named Onegin.
-
C.
historicalRelationship
Indicates a relationship that existed between entities in the past, often tied to a specific historical period, context, or event.
-
D.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
E.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
- 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_69e0b4f733f081908a401c0b7beb0b9f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c67974348190bd3484032c0d7b31 |
completed | April 21, 2026, 12:36 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a8dc148190b33ff51894e2a8f9 |
completed | April 20, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e5d53c4d6881909b4d0a716fa5ed4a |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 16, 2026, 12:46 p.m.