Triple
T8204385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regional Telecommunication Networks |
E191652
|
entity |
| Predicate | canBeSpecializedFor |
P76959
|
FINISHED |
| Object | Urban regions |
—
|
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: Urban regions | Statement: [Regional Telecommunication Networks, canBeSpecializedFor, Urban regions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBeSpecializedFor Context triple: [Regional Telecommunication Networks, canBeSpecializedFor, Urban regions]
-
A.
allowsSpecializationIn
chosen
Indicates that one entity grants permission or provides the option for another entity to pursue a specific specialization within it.
-
B.
canBeImplementedWith
Indicates that one entity is capable of being realized, executed, or fulfilled through the use or application of another entity.
-
C.
canBeTypeOf
Indicates that one entity is capable of serving as, or being classified as, a particular type or category of another entity.
-
D.
isMoreSpecificThan
Indicates that one concept represents a narrower, more detailed, or more constrained case of another concept.
-
E.
canBeAdaptedBy
Indicates that one entity is capable of being modified, adjusted, or tailored for use by another entity.
- 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_69ca82c7f3e08190857bf1fc63b2a10c |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb7268e2dc8190b630ea2bb75d0474 |
completed | March 31, 2026, 7:06 a.m. |
| PD | Predicate disambiguation | batch_69cb36ad01ac81909609b15f6a6c8581 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:43 p.m.