Triple
T15971815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judith |
E387342
|
entity |
| Predicate | hasMaritalStatusInStory |
P92865
|
FINISHED |
| Object | married woman |
—
|
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 woman | Statement: [Judith, hasMaritalStatusInStory, married woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaritalStatusInStory Context triple: [Judith, hasMaritalStatusInStory, married woman]
-
A.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
B.
characterMaritalHistory
Indicates a relationship that records the sequence of a character’s past and present marital relationships, including spouses and relevant time periods.
-
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.
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.
-
E.
hasMaritalStatusAtEnd
Indicates that an entity possesses a specific marital status at the end of a given period, event, or reference time.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.