Triple
T13386917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago bungalows |
E319466
|
entity |
| Predicate | typicalLotType |
P109716
|
FINISHED |
| Object | narrow city lot |
—
|
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: narrow city lot | Statement: [Chicago bungalows, typicalLotType, narrow city lot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalLotType Context triple: [Chicago bungalows, typicalLotType, narrow city lot]
-
A.
typicalUnitType
Indicates that one entity is the standard or commonly used unit type associated with measuring or expressing the other entity.
-
B.
typicalHouse
Indicates that something is a standard or representative example of a house in terms of its usual features, structure, or characteristics.
-
C.
typicalUnitConfiguration
Indicates the standard or commonly used arrangement, composition, or setup of a unit in a given context.
-
D.
roofTypeTypical
Indicates that a specified roof type is the common or characteristic roof style typically found for a given context or entity.
-
E.
typicalProductionType
Indicates the usual or characteristic type of production activity associated with an 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadce96d1881909957fdd068a7f55d |
completed | April 11, 2026, 11:44 p.m. |
| PD | Predicate disambiguation | batch_69d9a03189908190a784a2755f8d81e1 |
completed | April 11, 2026, 1:13 a.m. |
| PDg | Predicate description generation | batch_69dadcce5a808190847f2a7833b67a5a |
completed | April 11, 2026, 11:44 p.m. |
Created at: April 9, 2026, 9:34 p.m.