Triple
T7166905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwendoline Mary Lacey |
E167091
|
entity |
| Predicate | relationshipTypeWith Alicia Johns |
P75244
|
FINISHED |
| Object | classmate |
—
|
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: classmate | Statement: [Gwendoline Mary Lacey, relationshipTypeWith Alicia Johns, classmate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Alicia Johns Context triple: [Gwendoline Mary Lacey, relationshipTypeWith Alicia Johns, classmate]
-
A.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
B.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
C.
relationshipTypeWithLizzieEustace
Indicates the specific nature or category of relationship that an entity has with Lizzie Eustace.
-
D.
relationshipTypeWithJanieCrawford
Indicates the specific nature or category of relationship that an entity has with Janie Crawford.
-
E.
hasPoliticalRelationshipWith
Indicates a political connection or association between two entities, such as alliances, rivalries, collaborations, or other forms of political interaction.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e85a07388190a07054ef12870fa1 |
completed | March 27, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69c6e1cd5c948190a9113b23f7308c21 |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a213508190a40aca39f9eee7d5 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:48 p.m.