Triple
T28785163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piedras Negras |
E726785
|
entity |
| Predicate | hasIndustrialInfrastructure |
P57588
|
FINISHED |
| Object | industrial parks |
—
|
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: industrial parks | Statement: [Piedras Negras, hasIndustrialInfrastructure, industrial parks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIndustrialInfrastructure Context triple: [Piedras Negras, hasIndustrialInfrastructure, industrial parks]
-
A.
hasIndustrialDevelopment
chosen
Indicates that an entity possesses, supports, or is characterized by industrial growth, infrastructure, or manufacturing-related development.
-
B.
hasIndustrialTown
Indicates that an entity possesses or is associated with a town characterized primarily by industrial activities or facilities.
-
C.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
-
D.
hasIndustrialPlant
Indicates that an entity possesses, operates, or is associated with an industrial plant facility.
-
E.
containsIndustrialAreas
Indicates that one entity includes or encompasses industrial areas within its boundaries or scope.
- 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_69f0319aabec81908368720196f69a35 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69fe066d62b48190867df334039be786 |
completed | May 8, 2026, 3:51 p.m. |
| PD | Predicate disambiguation | batch_69fe03afde3c8190a5b9b0778d19eb1a |
completed | May 8, 2026, 3:39 p.m. |
Created at: April 28, 2026, 6:21 a.m.