Triple
T33284281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tabor |
E852136
|
entity |
| Predicate | primaryAntagonistRuler |
P29251
|
FINISHED |
| Object | Queen Ravenna |
E241099
|
NE 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: Queen Ravenna | Statement: [Tabor, primaryAntagonistRuler, Queen Ravenna]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryAntagonistRuler Context triple: [Tabor, primaryAntagonistRuler, Queen Ravenna]
-
A.
primaryAntagonistType
Indicates the role or category of the main opposing force or adversary that serves as the central source of conflict.
-
B.
primaryAntagonists
Indicates that the referenced entities serve as the main opposing or adversarial forces in relation to a specified subject or narrative.
-
C.
opponentMonarch
Indicates that one monarch stands in an opposing or adversarial position to another monarch, typically in conflict or competition.
-
D.
opposingMonarch
chosen
Indicates that two monarchs are in opposition to each other, typically as rivals, enemies, or leaders of conflicting factions or states.
-
E.
primaryAntagonisticRealmIn
Indicates that an entity’s main or most significant antagonistic or opposing activity occurs within a specified realm or domain.
- F. None of above.
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_69f349660ff48190a4568803d0b89941 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3576d575508190b7dee5ae0d4696ec |
completed | June 19, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:32 a.m.