Triple
T37843997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince Orlofsky |
E943552
|
entity |
| Predicate | relationshipToRosalinde |
P204421
|
FINISHED |
| Object | party host |
—
|
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: party host | Statement: [Prince Orlofsky, relationshipToRosalinde, party host]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToRosalinde Context triple: [Prince Orlofsky, relationshipToRosalinde, party host]
-
A.
relationshipToAlice
Indicates the specific type of relationship or connection that an entity has with Alice.
-
B.
relationshipToEleanorVance
Indicates the specific nature or type of relationship an entity has with Eleanor Vance.
-
C.
relationshipToTina
Indicates the specific type of personal or social relationship that an entity has with Tina.
-
D.
relationshipToNina
Indicates that one entity has a specified personal or social relationship to Nina.
-
E.
relationshipToPolinaAlexandrovna
Indicates the specific type of personal or social relationship that one entity has with Polina Alexandrovna.
- 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_69f76eeb0f7081908d6d3adbc469889c |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.