Triple
T6535356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yeongdodaegyo Bridge |
E152344
|
entity |
| Predicate | maximumLiftAngle |
P23303
|
FINISHED |
| Object | 75 degrees |
—
|
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: 75 degrees | Statement: [Yeongdodaegyo Bridge, maximumLiftAngle, 75 degrees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumLiftAngle Context triple: [Yeongdodaegyo Bridge, maximumLiftAngle, 75 degrees]
-
A.
maxVerticalAngle
chosen
Indicates the greatest vertical angular difference or tilt between two entities or directions.
-
B.
maximumTakeoffWeight
Indicates the greatest allowable weight an aircraft can have at the start of its takeoff roll under specified conditions.
-
C.
hasHighestLift
Indicates that the subject entity achieves the greatest lift value (e.g., improvement or elevation relative to a baseline) compared to all other relevant entities in the given context.
-
D.
hasLift
Indicates that one entity is equipped with, contains, or provides access to a lift (elevator).
-
E.
maximumGradient
Indicates the greatest rate of change or steepest slope that occurs within a given function, surface, or dataset.
- 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_69c688048ec8819093a47f7d332e12ec |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6adc05da88190b402085954cec8e0 |
completed | March 27, 2026, 4:18 p.m. |
| PD | Predicate disambiguation | batch_69c68abd9c7c819099e4fe8097cd1b28 |
completed | March 27, 2026, 1:48 p.m. |
Created at: March 27, 2026, 1:46 p.m.