Triple
T37376064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbary Lane boarding house |
E927983
|
entity |
| Predicate | landladyInFiction |
P107703
|
FINISHED |
| Object | Anna Madrigal |
—
|
NE NERFINISHED |
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: Anna Madrigal | Statement: [Barbary Lane boarding house, landladyInFiction, Anna Madrigal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landladyInFiction Context triple: [Barbary Lane boarding house, landladyInFiction, Anna Madrigal]
-
A.
landladyOf
chosen
Indicates that one person is the landlady (female landlord or property owner/manager) of another person, who is her tenant.
-
B.
fictionalResidence
Indicates that one entity is the place where another entity lives or is based within a fictional or imaginary context.
-
C.
residentInFiction
Indicates that one entity is a fictional character or element that resides or exists within the fictional setting, world, or universe represented by another entity.
-
D.
hostsFictional
Indicates that one entity serves as the setting, platform, or environment in which a fictional character, event, or work is situated or presented.
-
E.
houseOwnerInStory
Indicates that one entity is the owner of a house within the context or events of a particular story.
- 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_69f76eb820248190a5c395ca50ad002a |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb9e1845e881908d19158440cf3b87 |
completed | May 6, 2026, 8:01 p.m. |
| PD | Predicate disambiguation | batch_69fb8d08d6988190a00794ac26078348 |
completed | May 6, 2026, 6:48 p.m. |
Created at: May 3, 2026, 4:16 p.m.