Triple
T13076494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuang Si Falls |
E329588
|
entity |
| Predicate | approximateDistanceInKilometresFromLuangPrabang |
P108320
|
FINISHED |
| Object | 29 |
—
|
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: 29 | Statement: [Kuang Si Falls, approximateDistanceInKilometresFromLuangPrabang, 29]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateDistanceInKilometresFromLuangPrabang Context triple: [Kuang Si Falls, approximateDistanceInKilometresFromLuangPrabang, 29]
-
A.
distanceToVientiane_km
Indicates the physical distance, measured in kilometers, between a given location and Vientiane.
-
B.
distanceFromSihanoukville
Indicates the measured distance between a given location and Sihanoukville.
-
C.
distanceFromBangkok
Indicates the spatial distance between a given location and the city of Bangkok.
-
D.
distanceToHoChiMinhCity
Indicates the physical distance between a given location or entity and Ho Chi Minh City.
-
E.
distanceToDaNang_km
Indicates the distance, measured in kilometers, between a given location and Da Nang.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98117209081908272021013df2222 |
completed | April 10, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69d9803d46688190bac6b7d208f08d01 |
completed | April 10, 2026, 10:57 p.m. |
| PDg | Predicate description generation | batch_69d980e622e8819087a69bfb1660dd64 |
completed | April 10, 2026, 10:59 p.m. |
Created at: April 9, 2026, 9:01 p.m.