Triple
T14761814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dinah Barkley |
E346880
|
entity |
| Predicate | hasFictionalMaritalStatus |
P92865
|
FINISHED |
| Object | married |
—
|
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: married | Statement: [Dinah Barkley, hasFictionalMaritalStatus, married]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalMaritalStatus Context triple: [Dinah Barkley, hasFictionalMaritalStatus, married]
-
A.
fictionalMaritalStatus
chosen
Indicates that an entity has a marital status that exists only within a fictional, narrative, or hypothetical context rather than in real life.
-
B.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
C.
marital status
Indicates the legal or social state of a person’s marriage-related relationship, such as being single, married, divorced, or widowed.
-
D.
hasCivilStatus
Indicates the civil or marital status that applies to a person or entity (e.g., single, married, divorced).
-
E.
characterMaritalHistory
Indicates a relationship that records the sequence of a character’s past and present marital relationships, including spouses and relevant time periods.
- F. None of above.
Provenance (3 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f207dc819088a53f717736a121 |
completed | April 14, 2026, 11:04 p.m. |
| PD | Predicate disambiguation | batch_69de8c02e5c08190943c27594026faf7 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:30 a.m.