Triple
T1483980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Look-and-say sequence |
E29421
|
entity |
| Predicate | hasExampleTerm |
P1259
|
FINISHED |
| Object | 312211 |
—
|
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: 312211 | Statement: [Look-and-say sequence, hasExampleTerm, 312211]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExampleTerm Context triple: [Look-and-say sequence, hasExampleTerm, 312211]
-
A.
hasExample
chosen
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
B.
hasTerm
Indicates that an entity includes, is associated with, or is defined by a specific term or condition.
-
C.
hasNonExample
Indicates that something is associated with an instance that explicitly does not satisfy or illustrate a given concept, rule, or category.
-
D.
hasNumberOfTerms
Indicates the quantity of distinct terms or elements associated with a given entity or expression.
-
E.
includesExampleTaxon
Indicates that a taxonomic group or concept contains a specific taxon used as an illustrative or representative example.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c679714c8190ac53630fb49e19c5 |
completed | March 1, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69a4c486eacc81909c272f9bdf50a7c3 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:12 p.m.