Triple
T23611826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mister |
E583058
|
entity |
| Predicate | laterRelationshipWithCelie |
P115098
|
FINISHED |
| Object | friendlier and supportive |
—
|
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: friendlier and supportive | Statement: [Mister, laterRelationshipWithCelie, friendlier and supportive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterRelationshipWithCelie Context triple: [Mister, laterRelationshipWithCelie, friendlier and supportive]
-
A.
relationshipToCelie
chosen
Indicates a relational connection that someone or something has specifically with Celie, such as their role, bond, or association to her.
-
B.
relationshipTypeWithAibileenClark
Indicates the specific type or nature of the relationship that an entity has with Aibileen Clark.
-
C.
teachesCelie
Indicates that one entity provides instruction or education to Celie.
-
D.
relationshipToMissWatson
Indicates the type or nature of a person's relational connection to Miss Watson (e.g., familial, social, or other defined relationship).
-
E.
relationshipToCarolineCompson
Indicates the specific familial or social relationship that an entity has to Caroline Compson.
- 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_69e248fbcd9081908ba08913f9d30826 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b0f4606881908207114c5bcfe6a6 |
completed | April 29, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69f118d0e0588190a86527a7747c5427 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:45 p.m.