Triple
T19784197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asahel |
E475215
|
entity |
| Predicate | buriedAt |
P196
|
FINISHED |
| Object | Bethlehem |
—
|
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: Bethlehem | Statement: [Asahel, buriedAt, Bethlehem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bethlehem Context triple: [Asahel, buriedAt, Bethlehem]
-
A.
Bethlehem
chosen
Bethlehem is an ancient town in the West Bank historically revered as the birthplace of Jesus and a major center of Christian pilgrimage.
-
B.
Bethlehem
Bethlehem is a suburban town in Albany County, New York, known for its residential communities, schools, and proximity to the city of Albany.
-
C.
Bethlehem
Bethlehem is a small rural town in western Connecticut known for its historic charm and traditional New England character.
-
D.
Bethlehem
Bethlehem is a historic city in eastern Pennsylvania known for its former steel industry, vibrant arts scene, and role as home to Lehigh University.
-
E.
Bethlehem
Bethlehem is a town in the eastern Free State province of South Africa, known as an agricultural and commercial center in the region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65385ee8081908d58cc3ff05b9b23 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:49 p.m.