Triple
T19967219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince Yeletsky |
E479967
|
entity |
| Predicate | relationshipToHermann |
P138067
|
FINISHED |
| Object | rival in love |
—
|
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: rival in love | Statement: [Prince Yeletsky, relationshipToHermann, rival in love]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToHermann Context triple: [Prince Yeletsky, relationshipToHermann, rival in love]
-
A.
relationshipToManfred
Indicates the specific type of relationship or connection that one entity has to the individual named Manfred.
-
B.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
C.
relationshipToHermaphroditus
Indicates the specific familial, romantic, or social relationship that one entity has to Hermaphroditus.
-
D.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
-
E.
relationshipToJohanna
Indicates the specific type of relationship or connection that an entity has with Johanna.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc5e41881908c1e8867820f1c0c |
completed | April 20, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69e537f7e4848190b431a69ec3f1b609 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c42c688190a22f4d31ec692377 |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:54 p.m.