Triple
T22253321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surif |
E550032
|
entity |
| Predicate | locatedSouthOf |
P9676
|
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: [Surif, locatedSouthOf, Bethlehem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bethlehem Context triple: [Surif, locatedSouthOf, 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 suburb of Tauranga in New Zealand’s Bay of Plenty region, known for its strong Māori presence and community facilities, including marae associated with local iwi.
-
C.
Bethlehem
Bethlehem is the former name of what is now the city of Carmel in Hamilton County, Indiana.
-
D.
Bethlehem
Bethlehem is a small rural town in western Connecticut known for its historic charm and traditional New England character.
-
E.
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.
- 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_69e11e42adb8819087714772ea606709 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f138c0c4f48190a75473a7835014f1 |
completed | April 28, 2026, 10:46 p.m. |
Created at: April 16, 2026, 8:39 p.m.