Triple
T1554692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Media, Pennsylvania |
E33174
|
entity |
| Predicate | distanceToPhiladelphiaCenterCityMiles |
P2384
|
FINISHED |
| Object | about 13 |
—
|
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: about 13 | Statement: [Media, Pennsylvania, distanceToPhiladelphiaCenterCityMiles, about 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToPhiladelphiaCenterCityMiles Context triple: [Media, Pennsylvania, distanceToPhiladelphiaCenterCityMiles, about 13]
-
A.
distanceToPhiladelphia
chosen
Indicates the spatial distance between a given entity’s location and the city of Philadelphia.
-
B.
distanceToPennsylvaniaBorder
Indicates the measured distance between a given location and the border of Pennsylvania.
-
C.
distanceToWashingtonDC
Indicates the physical distance between a given location and Washington, D.C.
-
D.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
E.
distanceFromBoston
Indicates the spatial distance between a given entity’s location and the city of Boston.
- 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_69a885ee6db8819099502bc5ce8af881 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9407d9d1481909597af97b16512cc |
completed | March 5, 2026, 8:36 a.m. |
| PD | Predicate disambiguation | batch_69a907b688d081908171f89010c53973 |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:27 p.m.