Triple
T28212296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duke (The Notebook character) |
E711205
|
entity |
| Predicate | relationshipToAllie |
P201211
|
FINISHED |
| Object | husband |
—
|
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: husband | Statement: [Duke (The Notebook character), relationshipToAllie, husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToAllie Context triple: [Duke (The Notebook character), relationshipToAllie, husband]
-
A.
relationshipToAllison
Indicates the specific type of personal, familial, or social relationship that an entity has with Allison.
-
B.
relationshipToHallie
Indicates the specific type of personal or social relationship an entity has with Hallie.
-
C.
relationshipToAlly
Indicates the type or nature of a subject's connection or association with an ally.
-
D.
relationshipToAlcee
Indicates the specific type of relationship or connection that an entity has to Alcee.
-
E.
relationshipToMichelle
Indicates the specific type of relationship or connection that an entity has to Michelle.
- 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_69efb51cb5288190818c1f63a266af11 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69ffdf47d9608190830ca23d9cef6409 |
completed | May 10, 2026, 1:28 a.m. |
| PD | Predicate disambiguation | batch_69ffdf00e2b4819082dd5cb78f316baf |
completed | May 10, 2026, 1:27 a.m. |
| PDg | Predicate description generation | batch_69ffdf46e18c8190a5e4f4e5211cb087 |
completed | May 10, 2026, 1:28 a.m. |
Created at: April 27, 2026, 10:40 p.m.