Triple
T4170307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basil Fawlty |
E84545
|
entity |
| Predicate | relationshipWithSybilFawlty |
P54489
|
FINISHED |
| Object | acrimonious |
—
|
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: acrimonious | Statement: [Basil Fawlty, relationshipWithSybilFawlty, acrimonious]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithSybilFawlty Context triple: [Basil Fawlty, relationshipWithSybilFawlty, acrimonious]
-
A.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
-
B.
sibling
Indicates that two entities share at least one parent, making them brothers or sisters to each other.
-
C.
relationshipToSophie
Indicates the specific type of personal or social connection that an entity has to Sophie.
-
D.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
E.
relationshipToBenjy
Indicates the specific type of relationship or connection an entity has to Benjy.
- 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_69aed932cab48190b80ffe35f7029ae1 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02c87cc88190a9ec3712db18a8a7 |
completed | March 9, 2026, 5:26 p.m. |
| PD | Predicate disambiguation | batch_69af018fb0948190a9701b2e8e5d9bac |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af01ee94ec8190aa6dde54d4571c04 |
completed | March 9, 2026, 5:22 p.m. |
Created at: March 9, 2026, 3:44 p.m.