Triple
T29020635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duffless |
E737441
|
entity |
| Predicate | chronologicalPredecessorEpisode |
P160842
|
FINISHED |
| Object | I Love Lisa |
—
|
NE NERFINISHED |
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: I Love Lisa | Statement: [Duffless, chronologicalPredecessorEpisode, I Love Lisa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chronologicalPredecessorEpisode Context triple: [Duffless, chronologicalPredecessorEpisode, I Love Lisa]
-
A.
chronologyPrecedes
Indicates that one event or state occurs earlier in time than another in a chronological sequence.
-
B.
predecessorInMyth
Indicates that one mythological figure, story, or element chronologically or narratively comes before and influences or sets the stage for another within a mythic tradition.
-
C.
precedesInUniverse
chosen
Indicates that one event or entity occurs earlier than another within the timeline or causal order of a given fictional or conceptual universe.
-
D.
predecessorSeries
Indicates that one series directly precedes another in an ordered sequence or lineage.
-
E.
precedesInFilm
Indicates that one film is released or occurs earlier in sequence or narrative order than another film.
- 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_69f077ee19f881909af48f9cab00a2e5 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: April 28, 2026, 9:49 a.m.