Triple
T9277908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Military Service Act |
E222995
|
entity |
| Predicate | languageTension |
P87349
|
FINISHED |
| Object | heightened English–French Canadian tensions |
—
|
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: heightened English–French Canadian tensions | Statement: [Military Service Act, languageTension, heightened English–French Canadian tensions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageTension Context triple: [Military Service Act, languageTension, heightened English–French Canadian tensions]
-
A.
languageShift
Indicates a change in the primary language used by an entity, such as switching from one language to another over time or in a given context.
-
B.
languageUseTrend
Indicates how the use or prevalence of a particular language changes over time within a given population or context.
-
C.
languageUse
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
D.
languageOfExpression
Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
-
E.
languageIndependence
Indicates that a concept, method, or representation does not depend on any specific programming or natural language and can be applied uniformly across different languages.
- 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_69ca842123588190b3f2e1a69037d141 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd07cb28e081909b2754756c8f5dd0 |
completed | April 1, 2026, 11:55 a.m. |
| PD | Predicate disambiguation | batch_69cc7a576ec88190bbb787eb82e2e539 |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc94b796788190816b71b1e9996288 |
completed | April 1, 2026, 3:44 a.m. |
Created at: March 30, 2026, 7:34 p.m.