Triple
T18002061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taytay |
E430651
|
entity |
| Predicate | isRapidlyUrbanizing |
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: [Taytay, isRapidlyUrbanizing, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isRapidlyUrbanizing Context triple: [Taytay, isRapidlyUrbanizing, true]
-
A.
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.
-
B.
isUrbanized
Indicates that a place or area has been developed with dense human settlement, infrastructure, and built environment characteristic of a city or town.
-
C.
urbanizationLevel
Indicates the degree to which an area or population is characterized by urban development, infrastructure, and density of human settlement.
-
D.
isUrbanizedAround
Indicates that an area or region has developed urban characteristics or infrastructure surrounding a particular location or feature.
-
E.
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.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b3e9498c8190bdfa7a53b0c0d8db |
completed | April 19, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_69e3f90039e4819080527f860dca042e |
completed | April 18, 2026, 9:34 p.m. |
Created at: April 10, 2026, 10:23 a.m.