Triple
T9565701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophie Sheridan |
E230782
|
entity |
| Predicate | relationshipToHarryBright |
P89829
|
FINISHED |
| Object | possible daughter |
—
|
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: possible daughter | Statement: [Sophie Sheridan, relationshipToHarryBright, possible daughter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToHarryBright Context triple: [Sophie Sheridan, relationshipToHarryBright, possible daughter]
-
A.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
B.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
-
C.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
-
D.
relationshipToHeadmaster
Indicates the specific type of personal or professional relationship an entity has with the headmaster.
-
E.
relationshipToDudley
Indicates the specific familial or social relationship that one entity has to the person named Dudley.
- 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_69ca847f22188190a56e4a97625bef22 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd996c0a1081908a8356c454e60f74 |
completed | April 1, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69ccd594d0ac8190a81bc11a3a538167 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93e90048190a2b0d7c5c195ba98 |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:04 p.m.