Triple
T25655057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Munshir Diary |
E643209
|
entity |
| Predicate | hasSidekickCharacter |
P7748
|
FINISHED |
| Object | Topshe |
—
|
NE NERFINISHED |
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: Topshe | Statement: [Dr. Munshir Diary, hasSidekickCharacter, Topshe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSidekickCharacter Context triple: [Dr. Munshir Diary, hasSidekickCharacter, Topshe]
-
A.
formerSidekick
Indicates that one entity previously served as a sidekick or subordinate companion to another entity, but no longer holds that role.
-
B.
supportingCharacter
chosen
Indicates that one entity plays a secondary or assisting role in the story or context relative to another primary entity.
-
C.
hasSecondaryProtagonist
Indicates that an entity (such as a work of fiction) features another character who serves as a secondary or supporting main protagonist alongside the primary one.
-
D.
hasHumanPartnerCharacter
Indicates that an entity is associated with a partner character who is human.
-
E.
hasNumberOfCompanions
Indicates the quantity of companions or associates that an entity has.
- 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_69e77e7d8a848190a98d0162325fd780 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c6456608190b94e7c2e2c2a4824 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 21, 2026, 6:32 p.m.