Triple
T29437005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lien Khuong Airport |
E746600
|
entity |
| Predicate | distanceFromDaLatCityCenterKilometers |
P202511
|
FINISHED |
| Object | about 30 |
—
|
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: about 30 | Statement: [Lien Khuong Airport, distanceFromDaLatCityCenterKilometers, about 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromDaLatCityCenterKilometers Context triple: [Lien Khuong Airport, distanceFromDaLatCityCenterKilometers, about 30]
-
A.
distanceFromDaNangCenter
Indicates the spatial distance between an entity’s location and the central point of Da Nang.
-
B.
distanceToDaNang_km
Indicates the distance, measured in kilometers, between a given location and Da Nang.
-
C.
distanceFromCanThoCenter
Indicates the measured spatial distance between a given location and the central point of Can Tho.
-
D.
distanceFromHanoi
Indicates the spatial distance between a given location and Hanoi.
-
E.
distanceToHoChiMinhCity
Indicates the physical distance between a given location or entity and Ho Chi Minh City.
- 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_69f0a7a180e48190ae775e40047dbcb5 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_6a008c77d4dc8190b342d7407eaf48fa |
completed | May 10, 2026, 1:47 p.m. |
| PD | Predicate disambiguation | batch_6a008c18531c8190bbe883b73e6d023f |
completed | May 10, 2026, 1:46 p.m. |
| PDg | Predicate description generation | batch_6a008c770df481908fe58b265fba06a3 |
completed | May 10, 2026, 1:47 p.m. |
Created at: April 28, 2026, 3:17 p.m.