Triple
T23971850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles La Trobe |
E604253
|
entity |
| Predicate | travelledExtensivelyIn |
P154084
|
FINISHED |
| Object | North America |
—
|
NE NERFINISHED |
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: North America | Statement: [Charles La Trobe, travelledExtensivelyIn, North America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelledExtensivelyIn Context triple: [Charles La Trobe, travelledExtensivelyIn, North America]
-
A.
oftenTravelsTo
Indicates that one entity frequently goes to or visits another location or entity.
-
B.
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).
-
C.
oftenTravelsWith
Indicates that one entity frequently accompanies another entity when traveling or moving between places.
-
D.
travelsAbroad
Indicates that an entity goes to or spends time in a foreign country outside its usual nation of residence.
-
E.
visitedCountry
Indicates that an entity has traveled to and spent time in a particular country.
- 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_69e29543019c8190872462e593cc50b4 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d1dcef248190a04718f6f436dcc8 |
completed | April 29, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 9:25 p.m.