Triple
T10921224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chalk Sound |
E257950
|
entity |
| Predicate | distanceFromAirport |
P79745
|
FINISHED |
| Object | approximately 15–20 minutes by car |
—
|
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 15–20 minutes by car | Statement: [Chalk Sound, distanceFromAirport, approximately 15–20 minutes by car]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAirport Context triple: [Chalk Sound, distanceFromAirport, approximately 15–20 minutes by car]
-
A.
distanceToAirport
chosen
Indicates the measured distance between a given location and the nearest or specified airport.
-
B.
distanceToMBBAirport_km
Indicates the distance, measured in kilometers, from a given location to the MBB airport.
-
C.
nearestAirport
Indicates that one airport is the closest in distance to a given location or entity compared to all other airports.
-
D.
distanceToFrankfurtAirport_km
Indicates the physical distance, measured in kilometers, between a given location and Frankfurt Airport.
-
E.
nearbyAirportRelationship
Indicates that one location has an airport situated close enough to serve it conveniently, establishing a nearby-airport relationship between the two.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d77082a1488190850a4409339c3e1e |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d72e799f808190b6ab64fc7586a303 |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:22 p.m.