Triple
T38610497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ezra Mannon – Agamemnon archetype |
E934463
|
entity |
| Predicate | mirrorsCharacter |
P132767
|
FINISHED |
| Object | Agamemnon’s return from the Trojan War |
—
|
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: Agamemnon’s return from the Trojan War | Statement: [Ezra Mannon – Agamemnon archetype, mirrorsCharacter, Agamemnon’s return from the Trojan War]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mirrorsCharacter Context triple: [Ezra Mannon – Agamemnon archetype, mirrorsCharacter, Agamemnon’s return from the Trojan War]
-
A.
metCharacter
Indicates that one entity has encountered or been introduced to another entity at least once.
-
B.
parallelCharacter
chosen
Indicates that one character corresponds to or mirrors another character in a parallel role, function, or narrative pattern.
-
C.
mentorCharacter
Indicates that one character serves as a mentor, providing guidance, teaching, or support to another character.
-
D.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
E.
maimedCharacter
Indicates that one character has caused severe physical injury or mutilation to another character.
- 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_69f76eccd6d081909ccce171011739a1 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdb0de8c08190928cd1323f80ab5c |
completed | May 7, 2026, 6:33 p.m. |
| PD | Predicate disambiguation | batch_69fcd9017dd88190b32a73fe78909740 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.