Triple
T21961990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sete Lagoas |
E542352
|
entity |
| Predicate | hasAutomotiveManufacturing |
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: [Sete Lagoas, hasAutomotiveManufacturing, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAutomotiveManufacturing Context triple: [Sete Lagoas, hasAutomotiveManufacturing, true]
-
A.
hasAutomotiveIndustryBrand
Indicates that an entity possesses, is associated with, or operates a specific brand within the automotive industry.
-
B.
hasAutomotiveCluster
chosen
Indicates that an entity possesses or is associated with a concentration of automotive-related industries, organizations, or activities.
-
C.
enteredAutomobileProduction
Indicates that an entity began manufacturing automobiles as a commercial or industrial activity.
-
D.
usesManufacturingType
Indicates that one entity produces or processes something by applying a specific type or method of manufacturing.
-
E.
carManufacturer
Indicates that one entity is the company that produces or manufactures the car represented by the other 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_69e0c47fab1081908dc74a6545dbb051 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f124572738819098cc669aafa53cc6 |
completed | April 28, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69e6f601f2188190893bcdde0cf58ad6 |
completed | April 21, 2026, 3:58 a.m. |
Created at: April 16, 2026, 8 p.m.