Triple
T6731560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas |
E153644
|
entity |
| Predicate | hasMajorIndustryLinkedToMexico |
P13077
|
FINISHED |
| Object | maquiladora-related trade |
—
|
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: maquiladora-related trade | Statement: [Texas, hasMajorIndustryLinkedToMexico, maquiladora-related trade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMajorIndustryLinkedToMexico Context triple: [Texas, hasMajorIndustryLinkedToMexico, maquiladora-related trade]
-
A.
isLinkedEconomicallyTo
Indicates that two entities are connected through economic relationships such as trade, investment, financial flows, or shared market dependencies.
-
B.
hasMajorCountry
Indicates that an entity includes, is associated with, or is primarily represented by a particular major country.
-
C.
hasMajorOrganization
Indicates that an entity is associated with or primarily represented by a major organization.
-
D.
hasPrincipalIndustry
chosen
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
E.
haveMajorCrossBorderOrganization
Indicates that an entity possesses or is associated with a significant organization that operates across national borders.
- 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_69c6880bdd68819097de8b6099992682 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d354177481908ab3cf5437c095e2 |
completed | March 27, 2026, 6:58 p.m. |
| PD | Predicate disambiguation | batch_69c6d08e8a2c8190ae4e8d8c039be7ce |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:09 p.m.