Triple
T7622117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cape Liberty Cruise Port |
E172520
|
entity |
| Predicate | distanceToNewarkLibertyInternationalAirport |
P78162
|
FINISHED |
| Object | approximately 10 miles |
—
|
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: approximately 10 miles | Statement: [Cape Liberty Cruise Port, distanceToNewarkLibertyInternationalAirport, approximately 10 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToNewarkLibertyInternationalAirport Context triple: [Cape Liberty Cruise Port, distanceToNewarkLibertyInternationalAirport, approximately 10 miles]
-
A.
distanceToNewYorkCity
Indicates the spatial distance between a given entity’s location and New York City.
-
B.
distanceToPhiladelphia
Indicates the spatial distance between a given entity’s location and the city of Philadelphia.
-
C.
distanceToNewarkOnTrent
Indicates the measured distance between a given place or object and the location of Newark-on-Trent.
-
D.
distanceFromPennStation
Indicates the physical distance between a given location and Penn Station.
-
E.
distanceToPoughkeepsie
Indicates the spatial distance between a given entity and the location of Poughkeepsie.
- F. None of above. chosen
Provenance (4 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_69c699506b308190826894dab1d9ea86 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6fe73ff7c8190ab1218d97b37416d |
completed | March 27, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e725a88190b1f05dd224f7f4f2 |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6fe7323b0819081664662d2f26937 |
completed | March 27, 2026, 10:02 p.m. |
Created at: March 27, 2026, 3:56 p.m.