Triple
T19832685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | katabasis |
E476502
|
entity |
| Predicate | hasOppositeMotif |
P14351
|
FINISHED |
| Object | anabasis |
—
|
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: anabasis | Statement: [katabasis, hasOppositeMotif, anabasis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOppositeMotif Context triple: [katabasis, hasOppositeMotif, anabasis]
-
A.
hasOppositeStructure
Indicates that one entity possesses a structure that is the inverse or opposite in form, arrangement, or organization relative to another entity.
-
B.
hasOppositeDirectionTo
Indicates that one entity’s direction is exactly reversed or opposed to the direction of another entity.
-
C.
hasOpposingSide
Indicates that one entity possesses or is associated with another entity that lies on the opposite or facing side relative to a reference orientation or boundary.
-
D.
hasConceptualOpposite
Indicates that one entity represents a concept that is fundamentally opposed or contrary in meaning to the concept represented by another entity.
-
E.
reverseMotif
chosen
Indicates that one motif is the reversed or inverted form of another motif in structure, order, or direction.
- 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e656cf7e488190b4be28b5e7b363bf |
completed | April 20, 2026, 4:39 p.m. |
| PD | Predicate disambiguation | batch_69e5305bda388190a23b7191768107b1 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:50 p.m.