Triple
T13810281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wellesley Hills station |
E331869
|
entity |
| Predicate | distanceFromSouthStation |
P111565
|
FINISHED |
| Object | approximately 13 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 13 miles | Statement: [Wellesley Hills station, distanceFromSouthStation, approximately 13 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSouthStation Context triple: [Wellesley Hills station, distanceFromSouthStation, approximately 13 miles]
-
A.
distanceFromStation
Indicates the measured spatial separation between an entity and a specified station.
-
B.
distanceFromGrandCentral
Indicates the spatial distance between a given entity and Grand Central.
-
C.
distanceFromPennStation
Indicates the physical distance between a given location and Penn Station.
-
D.
distanceFromStPancras
Indicates the spatial distance between an entity and St Pancras.
-
E.
distanceToRailhead
Indicates the measured distance between a location or object and the nearest railhead (rail transport access point).
- 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_69d81c59f8808190a851bc56afdc55e9 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de026ff6b481908066d6bf27064417 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc862e9608190bd8a3d883959b7e4 |
completed | April 12, 2026, 4:29 p.m. |
| PDg | Predicate description generation | batch_69dcad0eea9881908f71e1eed9a2446b |
completed | April 13, 2026, 8:45 a.m. |
Created at: April 9, 2026, 10:12 p.m.