Triple
T2339290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government of Mexico City |
E44385
|
entity |
| Predicate | hasDemonymForResidents |
P191
|
FINISHED |
| Object | chilangos |
—
|
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: chilangos | Statement: [Government of Mexico City, hasDemonymForResidents, chilangos]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDemonymForResidents Context triple: [Government of Mexico City, hasDemonymForResidents, chilangos]
-
A.
hasDemonym
chosen
Indicates that one entity is the term (demonym) used to refer to the inhabitants or natives of another entity (typically a place).
-
B.
hasLanguageOfToponym
Indicates that a place name (toponym) is expressed in or associated with a particular language.
-
C.
supporterDemonym
Indicates the relationship between an entity and the demonym used to refer to its supporters or fans.
-
D.
hasExonym
Indicates that one entity is known by an alternative name or designation in another language or cultural context.
-
E.
populationDemonym
Indicates the term used to refer to the people or inhabitants associated with a particular place or region.
- 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_69a889132b488190bbb43ad4780ddd92 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6f75d888190a2e41edaa532e83f |
completed | March 7, 2026, 6:34 a.m. |
| PD | Predicate disambiguation | batch_69abc594087c819098100a10c5478a4b |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:51 p.m.