Triple
T17892405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hatun |
E447353
|
entity |
| Predicate | modernColloquialUsage |
P29199
|
FINISHED |
| Object | sometimes used informally for wife or woman |
—
|
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: sometimes used informally for wife or woman | Statement: [Hatun, modernColloquialUsage, sometimes used informally for wife or woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modernColloquialUsage Context triple: [Hatun, modernColloquialUsage, sometimes used informally for wife or woman]
-
A.
modernUse
Indicates how something is currently used or applied in modern times.
-
B.
modernUsageContext
Indicates the contemporary or current context in which something is used, applied, or functions.
-
C.
linguisticUsage
chosen
Indicates how a linguistic form, expression, or construction is used in language, such as its typical context, function, or register.
-
D.
contemporaryUse
Indicates that something is currently used or practiced in the present time or modern context.
-
E.
typicalLanguageUse
Indicates that one entity is the language most commonly or habitually used by another entity in ordinary communication or contexts.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49d7a855c8190b20bdbf6dcd4fd47 |
completed | April 19, 2026, 9:16 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:19 a.m.