Triple
T33307837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monumental Pictures |
E852783
|
entity |
| Predicate | HarlotsLanguage |
P103428
|
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: [Monumental Pictures, HarlotsLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: HarlotsLanguage Context triple: [Monumental Pictures, HarlotsLanguage, English]
-
A.
hasSlang
Indicates that one entity is an informal, colloquial, or slang term referring to the other entity.
-
B.
usesSlangFrom
Indicates that one entity incorporates or employs slang expressions originating from another entity or source.
-
C.
languageOfEpithet
Indicates the language in which an epithet (such as a descriptive or honorary title) is expressed.
-
D.
hasMeaningInEnglishSlang
chosen
Indicates that something (such as a word, phrase, or expression) carries a particular meaning specifically within English slang usage.
-
E.
usesDoubleEntendre
Indicates that one entity employs language or expressions with a double meaning, often to convey a hidden or suggestive message alongside a literal one.
- 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_69f349679fd8819093b9b40e989440e3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:33 a.m.