Triple
T34981364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul (Die tote Stadt) |
E1008822
|
entity |
| Predicate | relationshipWithMarietta |
P206780
|
FINISHED |
| Object | erotic obsession |
—
|
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: erotic obsession | Statement: [Paul (Die tote Stadt), relationshipWithMarietta, erotic obsession]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithMarietta Context triple: [Paul (Die tote Stadt), relationshipWithMarietta, erotic obsession]
-
A.
relationshipStatusWithMiaThermopolis
Indicates the type or state of a subject’s interpersonal relationship with Mia Thermopolis.
-
B.
relationshipToMarcy
Indicates that one entity has a specified personal or social relationship to Marcy.
-
C.
relationshipToMaria
Indicates the specific type of relationship or connection that an entity has to Maria.
-
D.
relationshipToFlagg
Indicates the specific type of personal or social relationship an entity has with the person or entity named Flagg.
-
E.
relationshipToOrlando
Indicates the specific familial, social, or other personal connection that one entity has to the person or place named Orlando.
- 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_69f76dc844a48190881951fffb83d17e |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379ff1ba081908eda86acefcf69fb |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:01 p.m.