Triple
T32479283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peruvian border control at Santa Rosa |
E830063
|
entity |
| Predicate | controlsTrafficType |
P146573
|
FINISHED |
| Object | traveler entry |
—
|
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: traveler entry | Statement: [Peruvian border control at Santa Rosa, controlsTrafficType, traveler entry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: controlsTrafficType Context triple: [Peruvian border control at Santa Rosa, controlsTrafficType, traveler entry]
-
A.
coversTrafficType
chosen
Indicates that one entity includes, handles, or applies to a specified type or category of traffic.
-
B.
managesTrafficFor
Indicates that one entity is responsible for directing, controlling, or regulating the flow of traffic for another entity or area.
-
C.
regulatesTrafficBetween
Indicates that one entity controls, manages, or directs the flow of traffic occurring between two other entities or locations.
-
D.
trafficType
Indicates the category or nature of traffic involved in a given interaction, flow, or connection (e.g., type of network, data, or transport traffic).
-
E.
hasTrafficMode
Indicates the mode or type of traffic associated with or applicable to an entity (e.g., pedestrian, vehicular, public transit).
- 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_69f3491ff3b48190b50a7fa00bb05b1f |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe21b0cba48190b56c39e9f1c0eafa |
completed | May 8, 2026, 5:47 p.m. |
| PD | Predicate disambiguation | batch_69fe204576848190aecf204e2adba5dc |
completed | May 8, 2026, 5:41 p.m. |
Created at: May 1, 2026, 12:58 a.m.