Triple
T16323975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milhouse Van Houten |
E396366
|
entity |
| Predicate | oftenPairsWith |
P69696
|
FINISHED |
| Object | Bart Simpson in storylines |
—
|
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: Bart Simpson in storylines | Statement: [Milhouse Van Houten, oftenPairsWith, Bart Simpson in storylines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenPairsWith Context triple: [Milhouse Van Houten, oftenPairsWith, Bart Simpson in storylines]
-
A.
oftenAccompaniedBy
Indicates that one entity is frequently found together with, occurs alongside, or is commonly associated in presence or use with another entity.
-
B.
commonPair
chosen
Indicates that two entities commonly occur together or are frequently associated as a pair in some shared context.
-
C.
usuallyAccompaniedBy
Indicates that one entity is commonly or habitually found together with, or occurs in the presence of, another entity.
-
D.
pairedWithFunctionally
Indicates that one entity is functionally matched or coupled with another to perform a complementary or corresponding role.
-
E.
pairedInDoubleBillWith
Indicates that two performances, films, or shows are scheduled or presented together as a combined double-feature program.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e296b8fe988190adee72b23246052f |
completed | April 17, 2026, 8:23 p.m. |
| PD | Predicate disambiguation | batch_69e219fc72c881909d452274e7af8238 |
completed | April 17, 2026, 11:31 a.m. |
Created at: April 10, 2026, 5:06 a.m.