Triple
T13609356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophie Lennon |
E325145
|
entity |
| Predicate | realPersona |
P61233
|
FINISHED |
| Object | wealthy woman living in a townhouse |
—
|
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: wealthy woman living in a townhouse | Statement: [Sophie Lennon, realPersona, wealthy woman living in a townhouse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: realPersona Context triple: [Sophie Lennon, realPersona, wealthy woman living in a townhouse]
-
A.
realPerson
Indicates that the referenced entity corresponds to an actual human individual, as opposed to a fictional, anonymous, or non-human entity.
-
B.
featuresRealPersonAsHimself
Indicates that a real person appears in the work portraying themself rather than a fictional character.
-
C.
realName
Indicates that one entity is the actual, full, or birth name of another entity, which may be known by an alias, nickname, or alternate identity.
-
D.
characterRealWorldCounterpart
Indicates that a fictional character is based on, inspired by, or directly corresponds to a specific real-world person.
-
E.
hasPersona
chosen
Indicates that an entity possesses or is associated with a particular persona, role, or character profile.
- F. None of above.
Provenance (3 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae1b3ee481909bd43ded6227a3e5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:50 p.m.