Triple
T5111878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Henry Dashwood |
E115230
|
entity |
| Predicate | spouseStatusAfterDeath |
P61663
|
FINISHED |
| Object | widowed |
—
|
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: widowed | Statement: [Mr. Henry Dashwood, spouseStatusAfterDeath, widowed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseStatusAfterDeath Context triple: [Mr. Henry Dashwood, spouseStatusAfterDeath, widowed]
-
A.
spouseDeathContext
Indicates the circumstances or contextual details surrounding the death of a person’s spouse.
-
B.
marriedToUntilDeathOfSpouse
Indicates a marital relationship that is intended to remain in effect until the death of one of the spouses.
-
C.
spouseDateOfDeath
Indicates the date on which a person's spouse died.
-
D.
spouseStatus
Indicates the marital relationship status between two individuals, such as whether they are currently spouses, formerly spouses, or not married to each other.
-
E.
spouseStatusAtMarriage
Indicates the marital status each partner held at the time their marriage to one another was formed.
- 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_69bd4441d1648190a54a533895041987 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd75ca57e881908242def2a032902e |
completed | March 20, 2026, 4:28 p.m. |
| PD | Predicate disambiguation | batch_69bd715fe3a8819087d3065adddba515 |
completed | March 20, 2026, 4:10 p.m. |
| PDg | Predicate description generation | batch_69bd72e1b7cc8190b2e621fdf8f22e38 |
completed | March 20, 2026, 4:16 p.m. |
Created at: March 20, 2026, 1:41 p.m.