Triple
T706926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hannibal Chau |
E14119
|
entity |
| Predicate | relationshipToProtagonists |
P10690
|
FINISHED |
| Object | reluctant ally |
—
|
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: reluctant ally | Statement: [Hannibal Chau, relationshipToProtagonists, reluctant ally]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToProtagonists Context triple: [Hannibal Chau, relationshipToProtagonists, reluctant ally]
-
A.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
B.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
C.
supportingCharacter
Indicates that one entity plays a secondary or assisting role in the story or context relative to another primary entity.
-
D.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
-
E.
wasCompanionOf
Indicates that one entity accompanied or associated closely with another, typically as a partner, ally, or fellow participant over some period of time.
- 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5c011948190b2cfccd8fe722742 |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f0217081908268b3f47e72f8df |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:36 p.m.