Triple
T7655314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acaponeta |
E173364
|
entity |
| Predicate | hasINEMunicipalityCode |
P3943
|
FINISHED |
| Object | 18001 |
—
|
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: 18001 | Statement: [Acaponeta, hasINEMunicipalityCode, 18001]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasINEMunicipalityCode Context triple: [Acaponeta, hasINEMunicipalityCode, 18001]
-
A.
hasMunicipalityCode
chosen
Indicates that an entity is associated with a specific official municipality code used for administrative or identification purposes.
-
B.
isInMunicipality
Indicates that one entity (typically a place or address) is located within the administrative boundaries of a specific municipality.
-
C.
hasMemberMunicipality
Indicates that an administrative region or governing body includes a specific municipality as one of its constituent members.
-
D.
hasNameInMunicipality
Indicates that an entity is known by a particular name within the context or jurisdiction of a specific municipality.
-
E.
hasMunicipalityType
Indicates that an administrative unit is classified as having a specific type or category of municipality (e.g., city, town, village).
- 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_69c6995473348190a4f41d110d619a18 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7061cbc3c8190a917dd7e71214182 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015dd8fc8190bc5f52a12bd46209 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 3:59 p.m.