Triple
T11325178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramos Arizpe |
E268192
|
entity |
| Predicate | hasAutomotiveCluster |
P98510
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Ramos Arizpe, hasAutomotiveCluster, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAutomotiveCluster Context triple: [Ramos Arizpe, hasAutomotiveCluster, true]
-
A.
hasAutomotiveIndustryBrand
Indicates that an entity possesses, is associated with, or operates a specific brand within the automotive industry.
-
B.
automotiveClassSupported
Indicates that a particular automotive class or category is supported or compatible within a given context or system.
-
C.
automotiveSegment
Indicates a classification relationship where an automotive product, brand, or activity is assigned to a specific market or vehicle segment within the automotive industry.
-
D.
hasVehicleFeature
Indicates that a vehicle possesses, includes, or is equipped with a specific feature or characteristic.
-
E.
hasMajorAutomakerHeadquarters
Indicates that a location serves as the primary corporate headquarters for a major automobile manufacturing company.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9e122e48190b3f890de8d561480 |
completed | April 9, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69d787ad575081908274280bf75d95fd |
completed | April 9, 2026, 11:04 a.m. |
| PDg | Predicate description generation | batch_69d796d049e88190a9fd7508f477f541 |
completed | April 9, 2026, 12:08 p.m. |
Created at: April 8, 2026, 9:32 p.m.