Triple
T31985408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richarda Smith |
E816707
|
entity |
| Predicate | marriedToAstronomerRoyal |
P145884
|
FINISHED |
| Object | George Biddell Airy |
—
|
NE NERFINISHED |
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: George Biddell Airy | Statement: [Richarda Smith, marriedToAstronomerRoyal, George Biddell Airy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToAstronomerRoyal Context triple: [Richarda Smith, marriedToAstronomerRoyal, George Biddell Airy]
-
A.
marriedToAstronomer
chosen
Indicates that one entity is married to another entity who is an astronomer.
-
B.
marriedToPhysicist
Indicates that a person is married to someone whose profession is physicist.
-
C.
marriedToFutureMonarch
Indicates that one person is married to another person who will become a monarch in the future.
-
D.
marriedToWidowedQueenOf
Indicates that one entity is married to a person who is the widowed queen of another specified entity (typically a realm or ruler).
-
E.
marriedToMonarchFrom
Indicates that a person is married to a monarch who rules or comes from a specified country or region.
- 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_69f348f6a3008190bfb59ca695fd68e2 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
Created at: May 1, 2026, 12:12 a.m.