Triple
T27226671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autopista Regional del Centro |
E682031
|
entity |
| Predicate | servesUrbanCenters |
P163466
|
FINISHED |
| Object | Maracay |
—
|
NE NERFINISHED |
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: Maracay | Statement: [Autopista Regional del Centro, servesUrbanCenters, Maracay]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesUrbanCenters Context triple: [Autopista Regional del Centro, servesUrbanCenters, Maracay]
-
A.
urbanCenterServed
chosen
Indicates that a particular urban center is provided with services or coverage by a specified entity (such as an infrastructure system, facility, or organization).
-
B.
isUrbanCentreFor
Indicates that one place functions as the primary urban hub or central city serving another area or population.
-
C.
developedUrbanCenters
Indicates that an entity has created, expanded, or significantly contributed to the growth of urban population centers or cities.
-
D.
connectsToUrbanCenter
Indicates that one entity has a direct or functional linkage to an urban center, such as through infrastructure, services, or regular interaction.
-
E.
majorEmploymentCentersServed
Indicates that the subject provides access or service to significant hubs of employment, such as large workplaces or business districts.
- 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_69eefacdad7881908b7bca61c90a1a1e |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
Created at: April 27, 2026, 9:44 a.m.