Triple
T25314008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lion of Lucerne |
E634681
|
entity |
| Predicate | numberOfNamesListed |
P22325
|
FINISHED |
| Object | approximately 760 |
—
|
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: approximately 760 | Statement: [Lion of Lucerne, numberOfNamesListed, approximately 760]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfNamesListed Context triple: [Lion of Lucerne, numberOfNamesListed, approximately 760]
-
A.
numberOfNames
chosen
Indicates the count of distinct names associated with a given entity.
-
B.
hasNumberOfNomesApprox
Indicates an approximate count of administrative regions or "nomes" associated with an entity.
-
C.
numberOfHeroesListed
Indicates the count of heroes that are specified or enumerated in a given context.
-
D.
hasApproximateNumberOfSurnames
Indicates that an entity is associated with an estimated or approximate count of surnames rather than an exact number.
-
E.
numberOfNamesOnWallOfMissing
Indicates the count of individual names that appear on a designated wall listing missing persons.
- 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_69e75a9847c08190bb02990d06d5ffb7 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f7516d5b4081908588a6feb541f355 |
completed | May 3, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69f74d40ebb081909daf60623e38f41d |
completed | May 3, 2026, 1:27 p.m. |
Created at: April 21, 2026, 1:27 p.m.