Triple
T26853504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pevensie siblings |
E676119
|
entity |
| Predicate | travelsVia |
P54144
|
FINISHED |
| Object | wardrobe to Narnia |
—
|
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: wardrobe to Narnia | Statement: [Pevensie siblings, travelsVia, wardrobe to Narnia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelsVia Context triple: [Pevensie siblings, travelsVia, wardrobe to Narnia]
-
A.
travelsThrough
chosen
Indicates that something moves along, passes across, or is routed via a particular path, medium, or location.
-
B.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
-
C.
traveledToBy
Indicates that an entity has been traveled to or visited by another entity.
-
D.
connectsTravelBetween
Indicates a relationship where something (such as a route, service, or mode of transport) enables or provides travel between two locations.
-
E.
traveledAs
Indicates that an entity moved from one place to another in the role, capacity, or identity specified by another entity (e.g., as a tourist, as a representative, as a refugee).
- 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_69eee9b9d7708190a15d7485709ae981 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f64dbbaefc8190952b8320bf4397d8 |
completed | May 2, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 5:19 a.m.