Triple
T4273786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biên Hòa |
E96997
|
entity |
| Predicate | distanceToHoChiMinhCity |
P55121
|
FINISHED |
| Object | approximately 30 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 30 kilometers | Statement: [Biên Hòa, distanceToHoChiMinhCity, approximately 30 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToHoChiMinhCity Context triple: [Biên Hòa, distanceToHoChiMinhCity, approximately 30 kilometers]
-
A.
distanceFromHanoi
Indicates the spatial distance between a given location and Hanoi.
-
B.
distanceToHoniaraApprox
Indicates an approximate distance measurement between a given entity’s location and the location of Honiara.
-
C.
distanceFromBangkok
Indicates the spatial distance between a given location and the city of Bangkok.
-
D.
distanceToPort-au-Prince
Indicates the spatial distance between a given location and the city of Port-au-Prince.
-
E.
distanceFromBeijing_km
Indicates the physical distance, measured in kilometers, between a given place or object and Beijing.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501abb74819086b2f04ac7a5c114 |
completed | March 12, 2026, 11:45 p.m. |
| PD | Predicate disambiguation | batch_69b347faa45481908c19c29fb906dc92 |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e0606488190baadf469a1afc3c2 |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:07 p.m.