Triple
T3632843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Antolini |
E76996
|
entity |
| Predicate | relationshipToHoldenCaulfield |
P38921
|
FINISHED |
| Object | former English teacher |
—
|
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: former English teacher | Statement: [Mr. Antolini, relationshipToHoldenCaulfield, former English teacher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToHoldenCaulfield Context triple: [Mr. Antolini, relationshipToHoldenCaulfield, former English teacher]
-
A.
relationshipToHuckFinn
Indicates the specific type of personal or social relationship an entity has to Huck Finn.
-
B.
relationshipToHuck
Indicates the specific type of personal or social relationship that one entity has with Huck.
-
C.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
D.
relationshipToHesterPrynne
Indicates the specific familial, social, or emotional connection that an entity has to Hester Prynne.
-
E.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
- 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_69ad85dd0be48190b738990cb20c4731 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc30457608190840fb5b33f9965c4 |
completed | March 8, 2026, 6:42 p.m. |
| PD | Predicate disambiguation | batch_69adb842be7c8190b7dfdb7c906f294c |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.