Triple
T9130982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wakanda |
E219083
|
entity |
| Predicate | transportTechnology |
P87289
|
FINISHED |
| Object | vibranium-powered maglev trains |
—
|
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: vibranium-powered maglev trains | Statement: [Wakanda, transportTechnology, vibranium-powered maglev trains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transportTechnology Context triple: [Wakanda, transportTechnology, vibranium-powered maglev trains]
-
A.
transportType
Indicates the mode or means of transportation used in carrying something or someone from one place to another.
-
B.
transports
Indicates that one entity carries or conveys another entity from one place to another.
-
C.
transportation
Indicates the movement of someone or something from one place to another, typically using a vehicle or transit system.
-
D.
mechanicalTransport
Indicates a relationship where an entity is moved or carried from one place to another using a mechanical means of transportation (e.g., vehicles, machines, or devices).
-
E.
transportationContext
Indicates the situational or environmental conditions under which transportation occurs, such as mode, purpose, or circumstances of travel.
- 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_69ca83debfc0819095800583e97ab10f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8ceea6c81909f368f12dac1649c |
completed | April 1, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69cc6601d77881908299d58db6e64937 |
completed | April 1, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69cc6a3c78388190a7436acc0e44ff55 |
completed | April 1, 2026, 12:43 a.m. |
Created at: March 30, 2026, 7:18 p.m.