Triple
T11621181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TRL |
E275639
|
entity |
| Predicate | setFeature |
P100634
|
FINISHED |
| Object | large street-facing windows overlooking Times Square |
—
|
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: large street-facing windows overlooking Times Square | Statement: [TRL, setFeature, large street-facing windows overlooking Times Square]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setFeature Context triple: [TRL, setFeature, large street-facing windows overlooking Times Square]
-
A.
featureSet
Indicates that one entity is a collection or configuration of features associated with or applied to another entity.
-
B.
featuresSetting
Indicates that something includes, presents, or highlights a particular setting as a notable or primary aspect.
-
C.
supportsFeature
Indicates that one entity provides, enables, or is compatible with a particular feature or capability of another.
-
D.
designedFeature
Indicates that one entity is a feature or component intentionally planned, created, or specified by another entity as part of a design.
-
E.
mayIncludeFeature
Indicates that one entity is allowed or able to contain, incorporate, or be associated with a particular feature.
- 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_69d6aaf84b548190ac072e4fb89ae18f |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a1206f1c81908d92024ef71958c0 |
completed | April 10, 2026, 7:05 a.m. |
| PD | Predicate disambiguation | batch_69d85dd6503c819081f9045e9d5c4f3f |
completed | April 10, 2026, 2:17 a.m. |
| PDg | Predicate description generation | batch_69d87f2e67108190ac36bf47aac12fa8 |
completed | April 10, 2026, 4:40 a.m. |
Created at: April 8, 2026, 9:39 p.m.