Triple
T5765852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eddie Carbone |
E127211
|
entity |
| Predicate | relationshipToCatherine |
P66259
|
FINISHED |
| Object | uncle by marriage |
—
|
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: uncle by marriage | Statement: [Eddie Carbone, relationshipToCatherine, uncle by marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToCatherine Context triple: [Eddie Carbone, relationshipToCatherine, uncle by marriage]
-
A.
relationshipToAnneBoleyn
Indicates the specific familial, marital, or social connection that an entity has to Anne Boleyn.
-
B.
relationshipToAuntEller
Indicates the specific familial relationship that an entity has to Aunt Eller (e.g., whether and how they are related to her).
-
C.
relationshipToSophie
Indicates the specific type of personal or social connection that an entity has to Sophie.
-
D.
historicalRelationship
Indicates a relationship that existed between entities in the past, often tied to a specific historical period, context, or event.
-
E.
relationshipToMissWatson
Indicates the type or nature of a person's relational connection to Miss Watson (e.g., familial, social, or other defined relationship).
- 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_69c00834f6308190851b0abeddd8ed7e |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02acb12c081908e4beee4a957f9f9 |
completed | March 22, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69c021ce8d3c81909b332cb1c33a61ad |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c02ac9603481909e3fa295d7904a15 |
completed | March 22, 2026, 5:45 p.m. |
Created at: March 22, 2026, 3:49 p.m.