Triple
T9545338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Downingtown, Pennsylvania |
E230267
|
entity |
| Predicate | partOfCommuterRegion |
P13163
|
FINISHED |
| Object | Philadelphia commuter belt |
—
|
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: Philadelphia commuter belt | Statement: [Downingtown, Pennsylvania, partOfCommuterRegion, Philadelphia commuter belt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfCommuterRegion Context triple: [Downingtown, Pennsylvania, partOfCommuterRegion, Philadelphia commuter belt]
-
A.
isCommuterRegionFor
chosen
Indicates that one region primarily serves as a residential base whose inhabitants regularly travel to another region for work or daily activities.
-
B.
partOfMetropolitanArea
Indicates that one place is included within and belongs to the larger metropolitan area of another place.
-
C.
regionOfCity
Indicates that a specified area or district is a constituent part or subdivision of a particular city.
-
D.
hasCommuterPopulation
Indicates that a place has a significant number of people who regularly travel to or from it for work, study, or other routine activities.
-
E.
operatesInMetropolitanArea
Indicates that an entity conducts its activities or provides its services within a specified metropolitan area.
- 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_69ca847c70b8819088a0a0bad64a50d6 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9901f2bc8190a4076f5947660df9 |
completed | April 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69ccd58bd21881908b860e3ee469af13 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:01 p.m.