Triple
T9540239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guernsey, Iowa |
E230133
|
entity |
| Predicate | hasRoadTransportMode |
P86086
|
FINISHED |
| Object | automobile |
—
|
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: automobile | Statement: [Guernsey, Iowa, hasRoadTransportMode, automobile]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRoadTransportMode Context triple: [Guernsey, Iowa, hasRoadTransportMode, automobile]
-
A.
hasTransportRoute
Indicates that there exists a designated transportation connection or route linking one entity to another.
-
B.
hasTransportationRelation
Indicates a relationship in which one entity provides, uses, is connected by, or is otherwise associated with a means or mode of transportation to another entity or location.
-
C.
hasPublicTransitMode
Indicates that a location, route, or service is associated with or supports a specific mode of public transportation (e.g., bus, train, tram).
-
D.
transportModePresent
chosen
Indicates that a particular mode of transportation is involved or available in the described context or event.
-
E.
hasTransportationSystem
Indicates that an entity possesses, operates, or is served by an organized system for transporting people or goods.
- 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_69ca847b1b3081908f72bc932c17cc41 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98e695948190ab107fff38c57de7 |
completed | April 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69ccd58bd21881908b860e3ee469af13 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:01 p.m.