Triple
T893971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosslyn, Arlington, Virginia |
E19300
|
entity |
| Predicate | skylineFeature |
P1495
|
FINISHED |
| Object | cluster of tall office and residential towers |
—
|
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: cluster of tall office and residential towers | Statement: [Rosslyn, Arlington, Virginia, skylineFeature, cluster of tall office and residential towers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: skylineFeature Context triple: [Rosslyn, Arlington, Virginia, skylineFeature, cluster of tall office and residential towers]
-
A.
featureType
Indicates the specific kind or category of feature that characterizes or distinguishes an entity.
-
B.
hasScenicViewOf
Indicates that one entity offers a visually appealing or picturesque view of another entity.
-
C.
keyFeature
Indicates that something is a primary, distinguishing, or most important feature of an entity.
-
D.
skeletonFeature
Indicates that one entity is a structural or anatomical skeletal feature or component of another entity.
-
E.
hasUrbanFeature
chosen
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.
- 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_69a4939d37188190848be3d426ebc9ae |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad212cd8819091eb1b7d606f5afd |
completed | March 1, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69a4aa94f7c881908deeb62308942e19 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.