Triple
T20995230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Henry Wotton |
E517128
|
entity |
| Predicate | hasRelationshipToDorianGray |
P142407
|
FINISHED |
| Object | mentor |
—
|
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: mentor | Statement: [Lord Henry Wotton, hasRelationshipToDorianGray, mentor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipToDorianGray Context triple: [Lord Henry Wotton, hasRelationshipToDorianGray, mentor]
-
A.
relationshipWithHumbertHumbert
Indicates that an entity has a specified type of personal, emotional, or social relationship with Humbert Humbert.
-
B.
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.
-
C.
relationshipToGustav von Aschenbach
Indicates the specific type of personal, social, or emotional connection an entity has to Gustav von Aschenbach.
-
D.
relationshipToBaudelaires
Indicates the type of personal or familial connection an entity has to the Baudelaires.
-
E.
relationshipToGoriot
Indicates the type or nature of a person's relationship to Goriot.
- 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_69e0b5006e2881909fc2383f841740cc |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc1fd5d48190a56981cee95ebd69 |
completed | April 21, 2026, 4:25 a.m. |
| PD | Predicate disambiguation | batch_69e5dbec80708190a49bccab7ff97e7b |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2df1a888190b5b478e76bdf7fdf |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 1:50 p.m.