Triple
T7508776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Singapore–Malaysia border |
E177459
|
entity |
| Predicate | hasHighTraffic |
P54842
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Singapore–Malaysia border, hasHighTraffic, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHighTraffic Context triple: [Singapore–Malaysia border, hasHighTraffic, yes]
-
A.
hasHeavyTraffic
chosen
Indicates that a location, route, or area is experiencing a high volume of traffic, causing congestion or delays.
-
B.
hasHeavyPassengerTraffic
Indicates that an entity experiences a high volume of passenger movement or usage over a given period.
-
C.
hasHigh
Indicates that an entity possesses a high level, degree, or intensity of a specified attribute or property.
-
D.
hasTruckTraffic
Indicates that there is truck-related vehicular movement or flow occurring on or through a specified location or route.
-
E.
hasTrafficPattern
Indicates that there is a characteristic or recurring flow of traffic associated with an entity, such as its typical volume, direction, or timing of movement.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5b8ab5c8190828ee8d144068828 |
completed | March 27, 2026, 9:25 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d44e9481909813e073b194f6f4 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:45 p.m.