Triple
T7684909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theodosia Burr Alston |
E174089
|
entity |
| Predicate | hasUncertainInformation |
P9778
|
FINISHED |
| Object | exact circumstances of disappearance and death |
—
|
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: exact circumstances of disappearance and death | Statement: [Theodosia Burr Alston, hasUncertainInformation, exact circumstances of disappearance and death]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUncertainInformation Context triple: [Theodosia Burr Alston, hasUncertainInformation, exact circumstances of disappearance and death]
-
A.
hasUncertainty
chosen
Indicates that the relationship or value is associated with some level or type of uncertainty rather than being fully definite or precise.
-
B.
hasUncertainNature
Indicates that the nature, status, or characteristics of the relationship or situation are not clearly defined, known, or determined.
-
C.
hasUncertainNumber
Indicates that the associated quantity or count is not known precisely or cannot be determined with certainty.
-
D.
hasCertainty
Indicates that a statement, belief, or relationship is associated with a specific level or degree of confidence or surety.
-
E.
hasUncertainVocabulary
Indicates that the relationship involves vocabulary whose meaning, usage, or interpretation is not clearly defined or is subject to doubt.
- 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_69c6995840408190a19de6c51090f46f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7048b0b448190889bd40e0a38e51a |
completed | March 27, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69c701618d3481908be84b76f36ac5a1 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 4:02 p.m.