Triple
T7383733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lavinia Chamberlayne |
E170326
|
entity |
| Predicate | hasMaritalStatusInWork |
P20884
|
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: [Lavinia Chamberlayne, hasMaritalStatusInWork, married]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaritalStatusInWork Context triple: [Lavinia Chamberlayne, hasMaritalStatusInWork, married]
-
A.
marital status
chosen
Indicates the legal or social state of a person’s marriage-related relationship, such as being single, married, divorced, or widowed.
-
B.
spouseStatus
Indicates the marital relationship status between two individuals, such as whether they are currently spouses, formerly spouses, or not married to each other.
-
C.
spouseStatusAtMarriage
Indicates the marital status each partner held at the time their marriage to one another was formed.
-
D.
hasMaritalRelationshipType
Indicates the specific type or nature of the marital relationship that exists between two entities.
-
E.
hasMarriage
Indicates a marital relationship exists between the two entities, specifying that they are or were legally married to each other.
- 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_69c68a5d0ed08190b6d361e68f813330 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1cb0b8881908132383c0efb0503 |
completed | March 27, 2026, 9:08 p.m. |
| PD | Predicate disambiguation | batch_69c6f02ee3e08190a7a00c981129b22c |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:08 p.m.