Triple
T38114811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louise Sawyer |
E951759
|
entity |
| Predicate | relationshipTypeWith Thelma Dickinson |
P207606
|
FINISHED |
| Object | best friends |
—
|
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: best friends | Statement: [Louise Sawyer, relationshipTypeWith Thelma Dickinson, best friends]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Thelma Dickinson Context triple: [Louise Sawyer, relationshipTypeWith Thelma Dickinson, best friends]
-
A.
relationshipTypeWith Dolly Talbo
Indicates the specific nature or category of the relationship that an entity has with Dolly Talbo.
-
B.
relationshipToJodieHolmes
Indicates the nature or type of relationship an entity has with Jodie Holmes.
-
C.
relationshipTypeWithDanielPlainview
Indicates the specific nature or category of relationship that an entity has with Daniel Plainview.
-
D.
relationshipTypeWithMildredPierce
Indicates the specific nature or category of the relationship an entity has with Mildred Pierce.
-
E.
hasRelationshipTypeWith Frank Drebin
Indicates that there exists a specific type of relationship between an entity and Frank Drebin.
- 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037df009f4819082e04683e6e8a106 |
completed | May 12, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:21 p.m.