Triple
T14384803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monsieur |
E356694
|
entity |
| Predicate | laterGeneralMeaning |
P86032
|
FINISHED |
| Object | polite form of address for a man in French |
—
|
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: polite form of address for a man in French | Statement: [Monsieur, laterGeneralMeaning, polite form of address for a man in French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterGeneralMeaning Context triple: [Monsieur, laterGeneralMeaning, polite form of address for a man in French]
-
A.
commonMeaning
Indicates that multiple entities share the same or very similar meaning or semantic interpretation.
-
B.
overallMeaning
chosen
Indicates the general or overarching significance, interpretation, or message conveyed by something as a whole.
-
C.
logicalMeaning
Indicates that one entity expresses, encodes, or conveys the logical content, implication, or formal meaning of another.
-
D.
possibleMeaning
Indicates that something may plausibly represent, signify, or be interpreted as a particular meaning or sense.
-
E.
literalMeaningApproximation
Indicates that one entity expresses an approximate or rough literal meaning of another entity, rather than an exact or fully precise interpretation.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de9025cff881908c08224d90d9f750 |
completed | April 14, 2026, 7:06 p.m. |
| PD | Predicate disambiguation | batch_69de2aa024c48190805df6a9d63deb10 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:16 a.m.