Triple
T32038718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neferu |
E818163
|
entity |
| Predicate | frequencyInCorpus |
P113578
|
FINISHED |
| Object | moderate among royal women |
—
|
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: moderate among royal women | Statement: [Neferu, frequencyInCorpus, moderate among royal women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyInCorpus Context triple: [Neferu, frequencyInCorpus, moderate among royal women]
-
A.
numberInCorpus
chosen
Indicates the numerical count or frequency with which a given item appears within a specified corpus.
-
B.
frequencyContent
Indicates that one entity specifies or characterizes the rate or frequency with which the content or occurrence of another entity takes place.
-
C.
frequencyGivenBy
Indicates that the frequency of something is specified, determined, or provided by a particular source or entity.
-
D.
frequencyRelation
Indicates a relationship that specifies how often one event, action, or state occurs in relation to another.
-
E.
frequencyClass
Indicates how often an event, action, or relation occurs, typically by assigning it to a predefined frequency category or class.
- 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_69f348fbc8148190b3c0f95d4772b153 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d26f27dc8190ae426a3e1573933e |
completed | May 3, 2026, 4:43 a.m. |
Created at: May 1, 2026, 12:19 a.m.