Triple
T3372497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Via 57 West |
E70985
|
entity |
| Predicate | hasCourtyardArea |
P48041
|
FINISHED |
| Object | approximately 22,000 square feet |
—
|
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: approximately 22,000 square feet | Statement: [Via 57 West, hasCourtyardArea, approximately 22,000 square feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCourtyardArea Context triple: [Via 57 West, hasCourtyardArea, approximately 22,000 square feet]
-
A.
hasCourtyard
Indicates that one entity includes, features, or is characterized by the presence of a courtyard.
-
B.
hasTerrace
Indicates that one entity includes, features, or is equipped with a terrace as part of its structure or property.
-
C.
hasStandingArea
Indicates that an entity includes or provides a designated area where people can stand.
-
D.
courtyardCapacity
Indicates the maximum number of entities that can be accommodated in a courtyard at the same time.
-
E.
courtyardShape
Indicates the geometric form or configuration that characterizes a courtyard.
- 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_69ad85a729d48190afd789cd8417f289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2bdcf70819087fc7e00fbd61e0d |
completed | March 8, 2026, 5:32 p.m. |
| PD | Predicate disambiguation | batch_69ada433059881908e46f38cc5f40a32 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69adaa518ac88190b64f949ace018ab7 |
completed | March 8, 2026, 4:56 p.m. |
Created at: March 8, 2026, 3:13 p.m.