Triple
T18078441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ridley Park, Pennsylvania |
E432619
|
entity |
| Predicate | hasNearbyEmployer |
P82033
|
FINISHED |
| Object | The Boeing Company Ridley Park plant |
—
|
LITERAL 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: The Boeing Company Ridley Park plant | Statement: [Ridley Park, Pennsylvania, hasNearbyEmployer, The Boeing Company Ridley Park plant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyEmployer Context triple: [Ridley Park, Pennsylvania, hasNearbyEmployer, The Boeing Company Ridley Park plant]
-
A.
hasMajorCompanyNearby
chosen
Indicates that a location or entity is situated close to at least one large or significant company.
-
B.
hasNearbyIndustry
Indicates that an entity is located close to one or more industrial facilities or activities.
-
C.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
D.
employerInRegion
Indicates that an employer operates or has its primary business presence within a specified geographic region.
-
E.
nearbyEconomicActivity
Indicates that there is economic activity occurring in close physical proximity to the referenced entity.
- F. None of above.
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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4d9f6a85481909894c39c8be98d5d |
completed | April 19, 2026, 1:34 p.m. |
| PD | Predicate disambiguation | batch_69e3f90c652481908133a73106d78919 |
completed | April 18, 2026, 9:35 p.m. |
Created at: April 10, 2026, 10:27 a.m.