Triple
T2770102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilbourntown |
E61433
|
entity |
| Predicate | hasTransportationRelation |
P43050
|
FINISHED |
| Object | near early Milwaukee harbor |
—
|
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: near early Milwaukee harbor | Statement: [Kilbourntown, hasTransportationRelation, near early Milwaukee harbor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransportationRelation Context triple: [Kilbourntown, hasTransportationRelation, near early Milwaukee harbor]
-
A.
hasTransportRoute
Indicates that there exists a designated transportation connection or route linking one entity to another.
-
B.
hasTransportationSystem
Indicates that an entity possesses, operates, or is served by an organized system for transporting people or goods.
-
C.
hasGroundTransportation
Indicates that an entity provides, includes, or is connected to transportation services or options that operate on land (e.g., cars, buses, trains).
-
D.
hasPublicTransitRole
Indicates that an entity holds a specific functional role or responsibility within a public transit system.
-
E.
transportAssumption
Indicates an assumption that something can be transported or carried from one place or context to another.
- 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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdcfed608819080988e93df7bdf7c |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abddcc348081908b5f760899389d4f |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 9:57 p.m.