Triple
T5409648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Handen station |
E120980
|
entity |
| Predicate | servesCommuterTraffic |
P19600
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Handen station, servesCommuterTraffic, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesCommuterTraffic Context triple: [Handen station, servesCommuterTraffic, yes]
-
A.
hasCommuterTraffic
chosen
Indicates that there is regular, recurring traffic flow associated with people traveling between their homes and places of work or study.
-
B.
servesGovernmentTraffic
Indicates that an entity provides services or functionality specifically for government-related network or data traffic.
-
C.
commuterHubFor
Indicates a location that serves as a primary transit or gathering point for commuters traveling to or from another place.
-
D.
roadTraffic
Indicates the presence, flow, or conditions of vehicles and movement along roads or streets.
-
E.
commutesBetween
Indicates a regular pattern of travel back and forth between two locations, typically for work, study, or routine activities.
- 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_69bd463a41cc8190b32ff5af2b96ca93 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8796a420819092c1771407cd1a5d |
completed | March 20, 2026, 5:44 p.m. |
| PD | Predicate disambiguation | batch_69bd8467e6b48190b9eaa9de67072e06 |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:05 p.m.