Triple
T4096801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palladian window |
E87840
|
entity |
| Predicate | hasTypicalLocation |
P21833
|
FINISHED |
| Object | upper stories of buildings |
—
|
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: upper stories of buildings | Statement: [Palladian window, hasTypicalLocation, upper stories of buildings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalLocation Context triple: [Palladian window, hasTypicalLocation, upper stories of buildings]
-
A.
typicalUseLocation
chosen
Indicates the usual or most common location where an entity is used or operates.
-
B.
hasMainLocationType
Indicates that an entity is associated with a primary or predominant type of location that characterizes where it is mainly situated or operates.
-
C.
hasLocationComponent
Indicates that something includes, is associated with, or is composed of a specific location-related part or element.
-
D.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
E.
typicalOnsetLocation
Indicates the anatomical location where a condition, symptom, or process most commonly begins or first appears.
- 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_69aed94564cc8190a9c1457daedb6e7f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefcdef11081908c626a89f2c0e121 |
completed | March 9, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69aef90b2ef08190ae84febfd69dd48b |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:40 p.m.