Triple
T10420081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harford Community College |
E245623
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Bel Air |
E862402
|
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: Bel Air | Statement: [Harford Community College, city, Bel Air]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bel Air Context triple: [Harford Community College, city, Bel Air]
-
A.
Bel Air
Bel Air is an affluent residential neighborhood on Los Angeles’s Westside, known for its large estates, exclusivity, and celebrity residents.
-
B.
Bel Air
chosen
Bel Air is a small historic town in Harford County, Maryland, serving as the county seat and regional center for government, commerce, and culture.
-
C.
Haddon Heights
Haddon Heights is a small suburban borough in southern New Jersey known for its historic homes, tree-lined streets, and close-knit community.
-
D.
Jefferson Hills
Jefferson Hills is a suburban borough in Allegheny County, Pennsylvania, located in the South Hills region near Pittsburgh.
-
E.
Bel Air, Los Angeles
Bel Air, Los Angeles is an affluent residential neighborhood on the Westside of Los Angeles known for its large estates, exclusivity, and celebrity residents.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea2aa7848190a7091ee71722fcc6 |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87e9b86648190b83eb5261c9a7b97 |
completed | April 10, 2026, 4:37 a.m. |
Created at: April 6, 2026, 12:11 p.m.