Triple
T10322569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Ford |
E242670
|
entity |
| Predicate | relationshipToJesseJames |
P38921
|
FINISHED |
| Object | gang member and later assassin of Jesse James |
—
|
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: gang member and later assassin of Jesse James | Statement: [Robert Ford, relationshipToJesseJames, gang member and later assassin of Jesse James]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToJesseJames Context triple: [Robert Ford, relationshipToJesseJames, gang member and later assassin of Jesse James]
-
A.
relationshipToJamesBrown
Indicates the type of personal, professional, or familial relationship that an entity has with James Brown.
-
B.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
C.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
D.
relationshipToQuentinJacobsen
Indicates the specific type of relationship or connection an entity has to Quentin Jacobsen.
-
E.
relationshipToBenjy
Indicates the specific type of relationship or connection an entity has to Benjy.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f64a648190a79980d647898eb0 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:50 a.m.