Triple
T11796982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dilogún |
E280525
|
entity |
| Predicate | hasNumberOfPrincipalOdu |
P101625
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [dilogún, hasNumberOfPrincipalOdu, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfPrincipalOdu Context triple: [dilogún, hasNumberOfPrincipalOdu, 16]
-
A.
hasNumberOfMainCables
Indicates the quantity of primary cables associated with or used by an entity.
-
B.
hasNumberOfDivisions
Indicates the relationship that specifies how many divisions or subunits an entity possesses.
-
C.
hasNumberOfPrakaras
Indicates the relationship specifying how many prakaras (enclosure layers or surrounding structures) are associated with a given entity.
-
D.
hasNumberOfCentres
Indicates the relationship specifying how many centers (or central units/locations) are associated with a given entity.
-
E.
hasNumberOfMainLights
Indicates the relationship that specifies how many primary or main lights are associated with an entity.
- F. None of above. chosen
Provenance (4 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_69d6ab258b808190b1735835c841e3a4 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a5a1cda0819092d66a82fd882786 |
completed | April 10, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69d8a2491f048190853239bc05090bf4 |
completed | April 10, 2026, 7:10 a.m. |
| PDg | Predicate description generation | batch_69d8a43cc0c881909fed7cd759fe90b1 |
completed | April 10, 2026, 7:18 a.m. |
Created at: April 8, 2026, 9:42 p.m.