Triple
T281079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | J |
E5354
|
entity |
| Predicate | connectsBorough |
P2564
|
FINISHED |
| Object | Queens to Manhattan |
—
|
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: Queens to Manhattan | Statement: [J, connectsBorough, Queens to Manhattan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsBorough Context triple: [J, connectsBorough, Queens to Manhattan]
-
A.
connectsCity
Indicates a relationship where one entity serves as a link or route that joins or provides direct access between two cities.
-
B.
hasBorough
Indicates that one entity is located within, belongs to, or is administratively part of a specific borough.
-
C.
numberOfBoroughs
Indicates the total count of boroughs associated with a given entity.
-
D.
connectsArea
chosen
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
-
E.
connectsTo
Indicates a relationship where one entity is linked or joined to another, allowing interaction, communication, or transfer between them.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e0a23c0819083abee28b2dea49c |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b77e028819087e606fc321219f7 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.