Triple
T23172548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Hare of Inaba |
E578895
|
entity |
| Predicate | geographicMotif |
P151209
|
FINISHED |
| Object | sea crossing |
—
|
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: sea crossing | Statement: [White Hare of Inaba, geographicMotif, sea crossing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geographicMotif Context triple: [White Hare of Inaba, geographicMotif, sea crossing]
-
A.
geographyBehavior
Indicates how an entity behaves, functions, or interacts in relation to its geographic context or location.
-
B.
geographicalMetaphor
Indicates a figurative relationship where spatial or geographic terms are used metaphorically to describe non-spatial concepts or relationships.
-
C.
geographicContext
Indicates that one entity is situated within, associated with, or characterized by the geographic setting or region defined by another entity.
-
D.
geographicalRepresentation
Indicates that one entity serves as a geographic depiction, model, or mapping of another entity’s location, area, or spatial characteristics.
-
E.
hasGeographyCharacteristic
Indicates that an entity possesses a specific geographical feature, property, or attribute.
- 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_69e245fd2a388190b814c0dfa15f7148 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18f30ce148190a6de928c8213399e |
completed | April 29, 2026, 4:55 a.m. |
| PD | Predicate disambiguation | batch_69ef89ff76808190808ee4ad9dea776b |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b75e2708190ba48875e36f983bc |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 4:04 p.m.