Triple
T28566688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Missy Cooper |
E722697
|
entity |
| Predicate | languageSpokenInUniverse |
P43064
|
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: [Missy Cooper, languageSpokenInUniverse, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageSpokenInUniverse Context triple: [Missy Cooper, languageSpokenInUniverse, English]
-
A.
languageOfFictionalUniverse
chosen
Indicates the language used or spoken within a fictional universe or setting.
-
B.
hasLanguageInUniverse
Indicates that a particular language exists or is used within a specified fictional or conceptual universe.
-
C.
fictionalUniverseLanguage
Indicates that a language is used or exists within a particular fictional universe.
-
D.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
E.
languageSpokenOnScreen
Indicates that a particular language is used in spoken dialogue or audible communication within an on-screen work (such as a film, show, or video).
- 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_69f01a5f69d08190ad5c0d2167078dec |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69fde49a084081909d99b1e0258169d5 |
completed | May 8, 2026, 1:26 p.m. |
| PD | Predicate disambiguation | batch_69fde1d04bd881909a46ecbbf18dfe59 |
completed | May 8, 2026, 1:14 p.m. |
Created at: April 28, 2026, 4:07 a.m.