Triple
T37094165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maggie Beckett |
E918511
|
entity |
| Predicate | travelsBetween |
P9205
|
FINISHED |
| Object | parallel universes |
—
|
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: parallel universes | Statement: [Maggie Beckett, travelsBetween, parallel universes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelsBetween Context triple: [Maggie Beckett, travelsBetween, parallel universes]
-
A.
connectsTravelBetween
Indicates a relationship where something (such as a route, service, or mode of transport) enables or provides travel between two locations.
-
B.
involvedTravelBetween
chosen
Indicates a relationship where an entity participates in or is associated with travel occurring between two specified locations.
-
C.
travelsThrough
Indicates that something moves along, passes across, or is routed via a particular path, medium, or location.
-
D.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
-
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_69f76e9a48bc8190a3947508d8bca408 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fbc36ce1f88190a7fa1656b714e107 |
completed | May 6, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69fbbd13595c81908719f52c3d37a7e8 |
completed | May 6, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:14 p.m.