Triple
T18855416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elves |
E461159
|
entity |
| Predicate | hasTypicalAppearance |
P133181
|
FINISHED |
| Object | pointed ears |
—
|
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: pointed ears | Statement: [Elves, hasTypicalAppearance, pointed ears]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalAppearance Context triple: [Elves, hasTypicalAppearance, pointed ears]
-
A.
typicalAppearanceContext
Indicates the usual situation, setting, or context in which an entity most commonly appears or is typically encountered.
-
B.
hasTypicalConditions
Indicates that something is associated with conditions or circumstances that are commonly or normally present for it.
-
C.
hasTypicalSide
Indicates that one entity is the usual or characteristic side (e.g., lateral aspect) associated with another entity.
-
D.
hasTypicalCut
Indicates that one entity is characterized by or associated with a standard or typical type of cut of another entity.
-
E.
hasCharacterAppearance
Indicates that a character appears or is visually represented within a given work, scene, or context.
- 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_69d8dcfb7b9c8190854e7b171b98ea2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c05c16e88190a08b6a8b94e1c9f9 |
completed | April 20, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e48d2166b88190add38de96cedc65c |
completed | April 19, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e49785fd7081909577e90a55df0a35 |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 10, 2026, 11:57 a.m.