Triple
T28666987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Professor George Gammell Angell |
E725609
|
entity |
| Predicate | relationshipToFrancisWaylandThurston |
P194115
|
FINISHED |
| Object | grand-uncle |
—
|
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: grand-uncle | Statement: [Professor George Gammell Angell, relationshipToFrancisWaylandThurston, grand-uncle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToFrancisWaylandThurston Context triple: [Professor George Gammell Angell, relationshipToFrancisWaylandThurston, grand-uncle]
-
A.
relationshipToFrancis
chosen
Indicates the specific familial, social, or professional connection that an entity has with Francis.
-
B.
relationshipToFrancisMarionTarwater
Indicates the specific familial, social, or other relational connection that an entity has to Francis Marion Tarwater.
-
C.
relationshipToNathanielHawthorne
Indicates the nature of a person or entity’s relationship or connection to Nathaniel Hawthorne.
-
D.
relationshipToThomasHardy
Indicates the specific familial, social, or professional relationship that one entity has to Thomas Hardy.
-
E.
hasRelationshipTypeWithFreddieThornhill
Indicates that an entity has a specific type of interpersonal relationship with Freddie Thornhill.
- 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_69f01d85be388190b669a0e401e2f2c4 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_6a0049e40d60819081e3899d8d9fca51 |
completed | May 10, 2026, 9:03 a.m. |
| PD | Predicate disambiguation | batch_6a0048c47b548190ad31b2901cc3784a |
completed | May 10, 2026, 8:58 a.m. |
Created at: April 28, 2026, 5:01 a.m.