Triple
T22923441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mon Valley |
E569226
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Glassport |
—
|
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: Glassport | Statement: [Mon Valley, contains, Glassport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Glassport Context triple: [Mon Valley, contains, Glassport]
-
A.
Glassport
chosen
Glassport is a small industrial borough in Allegheny County, Pennsylvania, located along the Monongahela River near Pittsburgh.
-
B.
Ambler
Ambler is a small Inupiat community and city in northwestern Alaska, located along the Kobuk River above the Arctic Circle.
-
C.
Steelmantown
Steelmantown is a small unincorporated community located within Upper Township in Cape May County, New Jersey.
-
D.
Waynesburg
Waynesburg is a small village in Stark County, Ohio, known for its rural character and tight-knit community.
-
E.
Phillipsburg
Phillipsburg is a town in western New Jersey situated along the Delaware River, known historically as a transportation and industrial hub opposite Easton, Pennsylvania.
- 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_69e2458f7d008190901dccbaebeaba24 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f180d7973c8190b09a5690fd1d3f28 |
completed | April 29, 2026, 3:53 a.m. |
Created at: April 17, 2026, 3:43 p.m.