Triple
T18245183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dubai Destination |
E436930
|
entity |
| Predicate | bestDistance |
P131008
|
FINISHED |
| Object | one mile |
—
|
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: one mile | Statement: [Dubai Destination, bestDistance, one mile]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestDistance Context triple: [Dubai Destination, bestDistance, one mile]
-
A.
nearestPass
Indicates that one entity is the closest in distance or proximity to another entity compared to all other possible entities or paths.
-
B.
closestTo
Indicates that one entity is nearer in distance to a reference entity than any other comparable entity.
-
C.
shortestNear
Indicates that one entity is the closest (or among the closest) to another entity compared to other nearby alternatives.
-
D.
primaryDistance
Indicates the main or most significant measure of distance between two entities in the relationship.
-
E.
nearestPointTo
Indicates the point that is closest in distance to a given reference point or object among a set of candidates.
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e6fbac8190bf252c4337f50c29 |
completed | April 19, 2026, 3:42 p.m. |
| PD | Predicate disambiguation | batch_69e44fcdee748190bae6fb76e0cb22f3 |
completed | April 19, 2026, 3:45 a.m. |
| PDg | Predicate description generation | batch_69e451a0ba208190a5fe92832a8f7a49 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 10:33 a.m.