Triple
T2200839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aileen Wuornos in Monster |
E50483
|
entity |
| Predicate | languageSpokenInFiction |
P7445
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Aileen Wuornos in Monster, languageSpokenInFiction, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageSpokenInFiction Context triple: [Aileen Wuornos in Monster, languageSpokenInFiction, English]
-
A.
languageOfBooks
Indicates the language in which the referenced books are written or published.
-
B.
areSpokenIn
chosen
Indicates that a particular language is used as a spoken means of communication within a specified region, community, or context.
-
C.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
D.
languageOfWritings
Indicates that a specified language is the one in which certain writings or written works are composed.
-
E.
languageCharacterizedBy
Indicates that a language is defined or distinguished by a particular feature, property, or characteristic.
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfa06bb4819092d7021358846e5f |
completed | March 7, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69abbda706f4819094de73e1d1d1f539 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.