Triple
T2021862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sieur Louis de Conte |
E44122
|
entity |
| Predicate | fictionalRelationship |
P34570
|
FINISHED |
| Object | childhood friend of Joan of Arc |
—
|
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: childhood friend of Joan of Arc | Statement: [Sieur Louis de Conte, fictionalRelationship, childhood friend of Joan of Arc]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalRelationship Context triple: [Sieur Louis de Conte, fictionalRelationship, childhood friend of Joan of Arc]
-
A.
portraysRelationship
Indicates that one entity depicts, represents, or illustrates a relationship between other entities.
-
B.
hasProtagonistRelationship
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
-
C.
historicalRelationship
Indicates a relationship that existed between entities in the past, often tied to a specific historical period, context, or event.
-
D.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
spouseOrLover
Indicates a romantic partnership between two entities, whether formalized as a spouse or existing as a lover.
- F. None of above. chosen
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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8efbe148190901d3650aa60408a |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb7a389408190a84a54856352f15b |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abb83e7888819096dc40275c77daff |
completed | March 7, 2026, 5:31 a.m. |
Created at: March 4, 2026, 7:38 p.m.