Triple
T6168572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Captain Disko Troop |
E137633
|
entity |
| Predicate | relationshipTypeWithHarveyCheyne |
P10690
|
FINISHED |
| Object | mentor‑student |
—
|
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: mentor‑student | Statement: [Captain Disko Troop, relationshipTypeWithHarveyCheyne, mentor‑student]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithHarveyCheyne Context triple: [Captain Disko Troop, relationshipTypeWithHarveyCheyne, mentor‑student]
-
A.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
-
C.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
D.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
E.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
- 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_69c008a68c508190a8d78245c865960e |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d8de56481909583104c70a52616 |
completed | March 22, 2026, 9:22 p.m. |
| PD | Predicate disambiguation | batch_69c055f5b81481908819515cdc334ae6 |
completed | March 22, 2026, 8:49 p.m. |
Created at: March 22, 2026, 4:18 p.m.