Triple
T189310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uptown Dallas |
E3682
|
entity |
| Predicate | urbanDevelopmentType |
P5744
|
FINISHED |
| Object | infill development |
—
|
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: infill development | Statement: [Uptown Dallas, urbanDevelopmentType, infill development]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanDevelopmentType Context triple: [Uptown Dallas, urbanDevelopmentType, infill development]
-
A.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
B.
buildingType
Indicates the specific category or function that characterizes what kind of building something is.
-
C.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
D.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
-
E.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
- F. None of above. chosen
Provenance (4 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a2594abeec8190a48f36817e647fcd |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a25672332081909386f35f3ca15dd2 |
completed | Feb. 28, 2026, 2:44 a.m. |
| PDg | Predicate description generation | batch_69a25738b5108190866fd704fceee18a |
completed | Feb. 28, 2026, 2:47 a.m. |
Created at: Feb. 28, 2026, 2:41 a.m.