Triple
T8501454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | To Kill a Mockingbird (1962 film) |
E201225
|
entity |
| Predicate | afis100HeroesVillainsRank |
P83076
|
FINISHED |
| Object | Atticus Finch ranked #1 hero |
—
|
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: Atticus Finch ranked #1 hero | Statement: [To Kill a Mockingbird (1962 film), afis100HeroesVillainsRank, Atticus Finch ranked #1 hero]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: afis100HeroesVillainsRank Context triple: [To Kill a Mockingbird (1962 film), afis100HeroesVillainsRank, Atticus Finch ranked #1 hero]
-
A.
featuresVillainActor
Indicates that the subject includes or presents an actor in the role of a villain.
-
B.
globalRankByDamage
Indicates the position of an entity in a worldwide ordering based on the amount of damage it has caused or dealt.
-
C.
FIFARankingWorst
Indicates that the subject has the lowest (worst) FIFA ranking among a specified set of entities.
-
D.
hasVillain
Indicates that one entity is the villain or primary antagonist associated with another entity.
-
E.
championRank
Indicates the relative level or position of an entity within a competitive ranking or championship hierarchy.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe59ad65c8190a2b8e6d22269853a |
completed | March 31, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cbd10a4b0881909e254117780dc823 |
completed | March 31, 2026, 1:50 p.m. |
| PDg | Predicate description generation | batch_69cbe30d453481908f897ed2b06e7534 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:14 p.m.