Triple
T89273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara Ann Delano Roosevelt |
E1793
|
entity |
| Predicate | travelHistory |
P1096
|
FINISHED |
| Object | traveled to Europe in youth |
—
|
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: traveled to Europe in youth | Statement: [Sara Ann Delano Roosevelt, travelHistory, traveled to Europe in youth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelHistory Context triple: [Sara Ann Delano Roosevelt, travelHistory, traveled to Europe in youth]
-
A.
travelPreference
Indicates a person's favored way or style of traveling, such as preferred modes, conditions, or arrangements for trips.
-
B.
tourismRegion
Indicates that a place or area is designated or recognized as a tourism region associated with another geographic or administrative entity.
-
C.
visitedBy
chosen
Indicates that a location or entity is the destination or target of a visit performed by another entity.
-
D.
explores
Indicates actively investigating, traveling through, or examining something in order to discover or learn more about it.
-
E.
experiences
Indicates that an entity undergoes, feels, or is affected by a particular event, state, or condition.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24feef1b08190bb9525f71cce053e |
completed | Feb. 28, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69a24eb82d408190b0f9c786152e8e4c |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:07 a.m.