Triple
T10115466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bobbsey Twins series |
E218344
|
entity |
| Predicate | featuresProtagonists |
P23263
|
FINISHED |
| Object | two sets of fraternal twins |
—
|
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: two sets of fraternal twins | Statement: [Bobbsey Twins series, featuresProtagonists, two sets of fraternal twins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresProtagonists Context triple: [Bobbsey Twins series, featuresProtagonists, two sets of fraternal twins]
-
A.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
B.
protagonistCharacteristic
Indicates that a characteristic, trait, or defining quality is attributed to the protagonist in a narrative or scenario.
-
C.
featuresCharacterRole
chosen
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
D.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
E.
protagonistBasedOn
Indicates that a fictional work’s main character is modeled on, inspired by, or derived from a particular real or fictional person or entity.
- 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_69ca83da93fc8190b54e44bc2b34857c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd161831c81908bb3c77caa7c3ce1 |
completed | April 2, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69cd4b9ed7e48190aa132ef8a69b49f9 |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:04 p.m.