Triple
T25508617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raphael Hythloday |
E639310
|
entity |
| Predicate | settingOfTravels |
P160924
|
FINISHED |
| Object | various parts of the New World (in fiction) |
—
|
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: various parts of the New World (in fiction) | Statement: [Raphael Hythloday, settingOfTravels, various parts of the New World (in fiction)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfTravels Context triple: [Raphael Hythloday, settingOfTravels, various parts of the New World (in fiction)]
-
A.
coTraveler
Indicates that two or more entities are traveling together along (part of) the same journey or route.
-
B.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
-
C.
travelScope
Indicates the extent or range within which travel is allowed, intended, or applicable for an entity or activity.
-
D.
travelers
Indicates that one or more entities are engaged in the activity or role of traveling, typically moving from one place to another.
-
E.
travelsAbroad
Indicates that an entity goes to or spends time in a foreign country outside its usual nation of residence.
- 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_69e75dbd09308190b6b5f0afdc12ec6d |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f60cd7b3e88190a1206958c0f0b225 |
completed | May 2, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69f60b8461ac81908c5bd3d73eed59f4 |
completed | May 2, 2026, 2:34 p.m. |
| PDg | Predicate description generation | batch_69f60c32ce088190a620eb59d2499fa9 |
completed | May 2, 2026, 2:37 p.m. |
Created at: April 21, 2026, 2:48 p.m.