Triple
T17006616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hundred Family Surnames |
E412586
|
entity |
| Predicate | originalNumberOfSurnames |
P22325
|
FINISHED |
| Object | 411 |
—
|
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: 411 | Statement: [Hundred Family Surnames, originalNumberOfSurnames, 411]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalNumberOfSurnames Context triple: [Hundred Family Surnames, originalNumberOfSurnames, 411]
-
A.
numberOfNames
chosen
Indicates the count of distinct names associated with a given entity.
-
B.
hasSurnameFrequency
Indicates that a surname occurs with a specified frequency or rate within a given population or dataset.
-
C.
isCountableAsSurname
Indicates that something can be considered or treated as a valid surname for counting or classification purposes.
-
D.
hasNumberOfNomesApprox
Indicates an approximate count of administrative regions or "nomes" associated with an entity.
-
E.
originalNumberOfLanguages
Indicates the initial count of distinct languages associated with an entity before any changes or reductions occur.
- 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_69d886cb581c8190ab05f4b429c9cd85 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d3831268819089286053a5acf653 |
completed | April 18, 2026, 6:54 p.m. |
| PD | Predicate disambiguation | batch_69e35d552bc08190af17ef7659e094ef |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:32 a.m.