Triple
T4736103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexico City (federal entity) |
E105129
|
entity |
| Predicate | numberOfAdministrativeDivisions |
P1679
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [Mexico City (federal entity), numberOfAdministrativeDivisions, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAdministrativeDivisions Context triple: [Mexico City (federal entity), numberOfAdministrativeDivisions, 16]
-
A.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
B.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
-
C.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
-
D.
politicalDivision
Indicates that one entity is a governmental or administrative subdivision or jurisdiction within the territory or authority of another entity.
-
E.
numberOfDistricts
chosen
Indicates the total count of districts associated with a given entity or area.
- 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_69bd43ee52048190b81a4f066534ffb3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64831c58819098758ac1f7839b3a |
completed | March 20, 2026, 3:15 p.m. |
| PD | Predicate disambiguation | batch_69bd6221c3b881908604f35f8de6f16b |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:19 p.m.