Triple
T25005706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Bobo |
E625835
|
entity |
| Predicate | livesInSameHouseAs |
P4704
|
FINISHED |
| Object |
Miss Spink and Miss Forcible
Miss Spink and Miss Forcible are two eccentric retired actresses who live together in the same old house as Coraline’s neighbors in Neil Gaiman’s novella "Coraline."
|
E1659610
|
NE FINISHED |
How this triple was built (3 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: Miss Spink and Miss Forcible | Statement: [Mr. Bobo, livesInSameHouseAs, Miss Spink and Miss Forcible]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Miss Spink and Miss Forcible Triple: [Mr. Bobo, livesInSameHouseAs, Miss Spink and Miss Forcible]
Generated description
Miss Spink and Miss Forcible are two eccentric retired actresses who live together in the same old house as Coraline’s neighbors in Neil Gaiman’s novella "Coraline."
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: livesInSameHouseAs Context triple: [Mr. Bobo, livesInSameHouseAs, Miss Spink and Miss Forcible]
-
A.
livedWith
Indicates that two entities shared the same residence or household for a period of time.
-
B.
hasHouseholdMember
Indicates that one entity is a member of the same household as another entity.
-
C.
livesUnder
Indicates that one entity resides beneath or is domiciled in a lower hierarchical or physical level relative to another entity.
-
D.
alsoLivesIn
chosen
Indicates that two or more entities share the same place of residence.
-
E.
hasFamilyHomeAt
Indicates that an entity has its family residence or primary household located at a specified place.
- F. None of above.
Provenance (6 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_69e2ff26c50481908bc82e799c9e6587 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f44b113f8c8190ae05102e9c2ae076 |
completed | May 1, 2026, 6:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1033730f0881908da32ad0591e7f8f |
completed | May 22, 2026, 10:44 a.m. |
| NEDg | Description generation | batch_6a103422072c8190949546db07c0b9bd |
completed | May 22, 2026, 10:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10351c0c0081909453f67b06668188 |
completed | May 22, 2026, 10:51 a.m. |
| PD | Predicate disambiguation | batch_69f442c0c2e88190acd7f170f10ccef6 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 6:05 a.m.