Triple
T35013114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Craglorn |
E1009983
|
entity |
| Predicate | travelConnection |
P148000
|
FINISHED |
| Object | Accessible from Bangkorai |
—
|
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: Accessible from Bangkorai | Statement: [Craglorn, travelConnection, Accessible from Bangkorai]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelConnection Context triple: [Craglorn, travelConnection, Accessible from Bangkorai]
-
A.
connectsTravelBetween
chosen
Indicates a relationship where something (such as a route, service, or mode of transport) enables or provides travel between two locations.
-
B.
travelMechanic
Indicates the method or system by which movement or travel between locations is carried out.
-
C.
travelRouteContext
Indicates the contextual details (such as purpose, conditions, or circumstances) under which a particular travel route is taken or defined.
-
D.
airTravelRelationWith
Indicates a relationship where one entity travels to, from, or between locations by air (e.g., via airplane or other aircraft) in connection with another entity.
-
E.
travelsFor
Indicates that one entity moves from place to place on behalf of, or for the benefit or purpose of, another entity or objective.
- 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_69f76dcc3ac8819096a3ed52f5fa2523 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7858aa5508190a07dde993b3356fc |
completed | May 3, 2026, 5:27 p.m. |
| PD | Predicate disambiguation | batch_69f7841812f081909d878955d114088e |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4:01 p.m.