Triple
T12359884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emma Gifford |
E294706
|
entity |
| Predicate | relationshipToThomasHardy |
P104754
|
FINISHED |
| Object | first wife |
—
|
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: first wife | Statement: [Emma Gifford, relationshipToThomasHardy, first wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToThomasHardy Context triple: [Emma Gifford, relationshipToThomasHardy, first wife]
-
A.
relationshipToLordEmsworth
Indicates the specific social or familial relationship that an entity has to Lord Emsworth.
-
B.
relationshipToIsabelArcher
Indicates the specific personal or social connection that an entity has to Isabel Archer.
-
C.
relationshipWithIsabelArcher
Indicates that there exists a specific kind of interpersonal relationship or connection between an entity and Isabel Archer.
-
D.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
E.
relationshipToGustav von Aschenbach
Indicates the specific type of personal, social, or emotional connection an entity has to Gustav von Aschenbach.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d942a2d6e08190a13c7ff89af09354 |
completed | April 10, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69d93ecf6b548190a394b6b56a0c1c68 |
completed | April 10, 2026, 6:17 p.m. |
| PDg | Predicate description generation | batch_69d9429ff2bc8190b09adf8f57fad451 |
completed | April 10, 2026, 6:34 p.m. |
Created at: April 8, 2026, 9:54 p.m.