Triple
T27026788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don't Say Gay law |
E680808
|
entity |
| Predicate | languageCharacterizedAs |
P24616
|
FINISHED |
| Object | vague by critics |
—
|
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: vague by critics | Statement: [Don't Say Gay law, languageCharacterizedAs, vague by critics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageCharacterizedAs Context triple: [Don't Say Gay law, languageCharacterizedAs, vague by critics]
-
A.
languageCharacterizedBy
chosen
Indicates that a language is defined or distinguished by a particular feature, property, or characteristic.
-
B.
legalCharacterization
Indicates how an action, event, or situation is classified or characterized under a specific legal framework or set of laws.
-
C.
linguisticClassification
Indicates the relationship by which an entity is categorized according to its language or linguistic type.
-
D.
linguisticClassificationBasis
Indicates the criterion or principle used as the basis for classifying something within a linguistic system or framework.
-
E.
linguisticType
Indicates the type or category of language or linguistic system associated with an entity (e.g., spoken, signed, written, or other linguistic modality).
- 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_69eeeb5450988190bfc9a3c012ac463a |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
Created at: April 27, 2026, 7:11 a.m.