Triple
T10531923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lolita |
E248462
|
entity |
| Predicate | relationshipWithHumbertHumbert |
P94405
|
FINISHED |
| Object | stepdaughter |
—
|
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: stepdaughter | Statement: [Lolita, relationshipWithHumbertHumbert, stepdaughter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithHumbertHumbert Context triple: [Lolita, relationshipWithHumbertHumbert, stepdaughter]
-
A.
relationshipWithMiladyDeWinter
Indicates a personal or interpersonal connection that an entity has with Milady de Winter, such as familial, romantic, adversarial, or other significant relational ties.
-
B.
relationshipToGustav von Aschenbach
Indicates the specific type of personal, social, or emotional connection an entity has to Gustav von Aschenbach.
-
C.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
-
D.
hasConcubineFrom
Indicates that a person has a concubine whose origin or affiliation is from a specified place or source.
-
E.
relationshipToPatsey
Indicates the nature or type of relationship an entity has with the person or entity named Patsey.
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50a17f23081909f3372e160e21670 |
completed | April 7, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_69d4fb9729288190a0149f127acd7ae3 |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d4fe06d4a48190b1a45dd1d4e16df0 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:30 p.m.