Triple
T17554997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tlaltizapán de Zapata Municipality |
E427567
|
entity |
| Predicate | hasPredominantlyRuralCommunities |
P47416
|
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: [Tlaltizapán de Zapata Municipality, hasPredominantlyRuralCommunities, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPredominantlyRuralCommunities Context triple: [Tlaltizapán de Zapata Municipality, hasPredominantlyRuralCommunities, true]
-
A.
isPredominantlyRural
chosen
Indicates that a place or region is characterized mainly by rural features, such as low population density and extensive non-urban land use.
-
B.
hasRuralFocus
Indicates that the subject is oriented toward, concerned with, or primarily serving rural areas or rural-related issues.
-
C.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
D.
hasRuralCommunes
Indicates that an entity possesses, includes, or is associated with one or more rural communes.
-
E.
hasLongRuralSectionsIn
Indicates that something (such as a route or infrastructure) contains extended stretches that pass through rural areas within a specified region or location.
- 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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4562205c08190a7580a762d61b1e3 |
completed | April 19, 2026, 4:12 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fb39948190a82a597c5bac5c57 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:50 a.m.