Triple
T7699545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zapopan |
E174452
|
entity |
| Predicate | hasRapidUrbanGrowth |
P37508
|
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: [Zapopan, hasRapidUrbanGrowth, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRapidUrbanGrowth Context triple: [Zapopan, hasRapidUrbanGrowth, true]
-
A.
hasUrbanGrowthCharacteristic
Indicates that an entity exhibits a particular quality, pattern, or feature related to urban growth or expansion.
-
B.
hasSuburbanGrowth
Indicates that an area or entity is experiencing or characterized by expansion or development typical of suburban environments.
-
C.
hasHighestUrbanizationRateIn
Indicates that the subject has the greatest proportion of its population living in urban areas compared to all other entities within the specified object region or group.
-
D.
isUrbanizing
chosen
Indicates a process in which an area or population becomes more urban in character, typically through increased development, infrastructure, and concentration of people and activities.
-
E.
urbanizationLevel
Indicates the degree to which an area or population is characterized by urban development, infrastructure, and density of human settlement.
- 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_69c6995a72cc8190998e56daa6f8e453 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70402169481909b219dc5f4a64b9b |
completed | March 27, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69c70165e78c8190bf6b3c34e243cb81 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:03 p.m.