Triple
T6826701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wise Men |
E157032
|
entity |
| Predicate | visited |
P2694
|
FINISHED |
| Object | Bethlehem |
E8382
|
NE 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: Bethlehem | Statement: [Wise Men, visited, Bethlehem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bethlehem Context triple: [Wise Men, visited, 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 small rural town in western Connecticut known for its historic charm and traditional New England character.
-
C.
Bethlehem
Bethlehem is a suburban town in Albany County, New York, known for its residential communities, schools, and proximity to the city of Albany.
-
D.
Beit She'an
Beit She'an is an ancient city in northeastern Israel known for its extensive archaeological remains, including a well-preserved Roman-Byzantine ruins complex.
-
E.
Hebron
Hebron is a small New England town in central Connecticut known for its rural character, historic charm, and annual agricultural fair.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c6882a5b5c8190917a7db9ed36bad1 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d58375248190935dd38d618994e3 |
completed | March 27, 2026, 7:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72f901e848190a8240c23bccc4cbc |
completed | March 28, 2026, 1:32 a.m. |
Created at: March 27, 2026, 2:18 p.m.