Triple
T31881567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Forest of Cedars |
E813895
|
entity |
| Predicate | travelDescriptor |
P174965
|
FINISHED |
| Object | very distant from human cities |
—
|
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: very distant from human cities | Statement: [Forest of Cedars, travelDescriptor, very distant from human cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelDescriptor Context triple: [Forest of Cedars, travelDescriptor, very distant from human cities]
-
A.
journeyDestination
Indicates that one entity serves as the endpoint or intended destination of another entity’s journey or travel.
-
B.
travelScope
Indicates the extent or range within which travel is allowed, intended, or applicable for an entity or activity.
-
C.
travelRouteContext
Indicates the contextual details (such as purpose, conditions, or circumstances) under which a particular travel route is taken or defined.
-
D.
travelMechanic
Indicates the method or system by which movement or travel between locations is carried out.
-
E.
travelRouteOf
Indicates the path or itinerary that an entity follows or uses when traveling from one location to another.
- 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_69f348ed74bc81909846aaa6a3c7318c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6cd9bae8c8190b528641499162a75 |
completed | May 3, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1470808190b70cdfd7a6395670 |
completed | May 3, 2026, 4:16 a.m. |
| PDg | Predicate description generation | batch_69f6cd119cac8190a0b3ebe8b9c742c2 |
completed | May 3, 2026, 4:20 a.m. |
Created at: April 30, 2026, 11:56 p.m.