Triple
T35016193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joshua and Margaret Investigations |
E1010059
|
entity |
| Predicate | focusesOnCharacters |
P77485
|
FINISHED |
| Object | Joshua |
—
|
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: Joshua | Statement: [Joshua and Margaret Investigations, focusesOnCharacters, Joshua]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusesOnCharacters Context triple: [Joshua and Margaret Investigations, focusesOnCharacters, Joshua]
-
A.
characterInFocus
chosen
Indicates that a particular character is the primary subject or focal point within a given context, scene, or narrative segment.
-
B.
characterizationFocus
Indicates that the primary emphasis or concern of a characterization is directed toward a particular aspect, feature, or dimension of the subject.
-
C.
targetsCharacter
Indicates that one entity is the intended focus or target of another entity’s action, effect, or behavior.
-
D.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
E.
characterAddressed
Indicates that one character directs speech, communication, or attention specifically toward 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_69f76dcc3ac8819096a3ed52f5fa2523 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd19f791f48190bbb6f6047f9ddc59 |
completed | May 7, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69fd0df365948190bc9bfc7ffd46acd8 |
completed | May 7, 2026, 10:10 p.m. |
Created at: May 3, 2026, 4:01 p.m.