Triple
T13684759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gubeikou |
E328092
|
entity |
| Predicate | distanceFromBeijingCenter |
P48910
|
FINISHED |
| Object | approximately 120 kilometers |
—
|
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 120 kilometers | Statement: [Gubeikou, distanceFromBeijingCenter, approximately 120 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromBeijingCenter Context triple: [Gubeikou, distanceFromBeijingCenter, approximately 120 kilometers]
-
A.
distanceFromBeijingCityCenter
chosen
Indicates the physical distance between an entity’s location and the geographic center of Beijing city.
-
B.
distanceFromBeijing_km
Indicates the physical distance, measured in kilometers, between a given place or object and Beijing.
-
C.
distanceFromTokyo
Indicates the physical distance between a given location and Tokyo.
-
D.
distanceFromParisCenter
Indicates the measured distance between a given location and the central point of Paris.
-
E.
distanceFromMoscow_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc66f8acc8190b2a82b722930b995 |
completed | April 12, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69dbbe9059488190a8113177c83e1481 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:53 p.m.