Triple
T15095569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Tyrone |
E360526
|
entity |
| Predicate | relationshipTypeWithMaryTyrone |
P117289
|
FINISHED |
| Object | loving but deeply strained 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: loving but deeply strained marriage | Statement: [James Tyrone, relationshipTypeWithMaryTyrone, loving but deeply strained marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithMaryTyrone Context triple: [James Tyrone, relationshipTypeWithMaryTyrone, loving but deeply strained marriage]
-
A.
relationshipTypeWithNinaSayers
Indicates the specific nature or category of relationship that an entity has with Nina Sayers.
-
B.
relationshipToPatsey
Indicates the nature or type of relationship an entity has with the person or entity named Patsey.
-
C.
hasRelationshipTypeWith Vince Tyler
Indicates that an entity is connected to Vince Tyler by a specific, characterized type of relationship.
-
D.
relationshipToCelie
Indicates a relational connection that someone or something has specifically with Celie, such as their role, bond, or association to her.
-
E.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005466e9c8190a68e1fbeb8922b1a |
completed | April 15, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec71e8dcc81908badc834b6ccf273 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:04 a.m.