Triple
T34109933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyaung U Airport |
E874810
|
entity |
| Predicate | hasPrimaryPassengers |
P203895
|
FINISHED |
| Object | tourists visiting Bagan |
—
|
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: tourists visiting Bagan | Statement: [Nyaung U Airport, hasPrimaryPassengers, tourists visiting Bagan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryPassengers Context triple: [Nyaung U Airport, hasPrimaryPassengers, tourists visiting Bagan]
-
A.
hasThroughPassengersWith
Indicates that two transportation segments, services, or locations are connected by passengers who travel through them without starting or ending their journey there.
-
B.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
C.
isPassengerWith
Indicates that one entity is traveling together with another entity as a passenger in the same vehicle or conveyance.
-
D.
hasPassengerOperations
Indicates that an entity conducts or supports transportation services specifically for carrying passengers.
-
E.
hasPassengerUsageCategory
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
- 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_69f349a80d4481908527317d43f5c579 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a0245e917748190bf0a65db538aa66f |
completed | May 11, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_6a023f7cf7148190af5c2ea501511145 |
completed | May 11, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_6a0245e83d5c8190b3cf9a5ea17c5d9c |
completed | May 11, 2026, 9:11 p.m. |
Created at: May 1, 2026, 1:53 a.m.