Triple
T33348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spanish |
E664
|
entity |
| Predicate | hasSpecialCharacter |
P2271
|
FINISHED |
| Object | ñ |
—
|
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: ñ | Statement: [Spanish, hasSpecialCharacter, ñ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpecialCharacter Context triple: [Spanish, hasSpecialCharacter, ñ]
-
A.
hasBasicLetters
Indicates that an entity contains or is composed of fundamental alphabetic characters, without additional symbols or diacritics.
-
B.
hasSpecialUnit
Indicates that an entity possesses or is associated with a distinct, designated unit that has a special role, function, or status.
-
C.
hasCharacteristic
Indicates that an entity possesses, exhibits, or is defined by a particular attribute, feature, or quality.
-
D.
hasUppercaseAndLowercase
Indicates that a string or text value contains at least one uppercase letter and at least one lowercase letter.
-
E.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
- 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_69a2479dec388190967ba648663442c9 |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a2496ffc548190b545f998cbebd5b9 |
completed | Feb. 28, 2026, 1:48 a.m. |
| PD | Predicate disambiguation | batch_69a248717f5081909952a8c9ed1e1742 |
completed | Feb. 28, 2026, 1:44 a.m. |
| PDg | Predicate description generation | batch_69a2496f21708190a0fd33e269b9917f |
completed | Feb. 28, 2026, 1:48 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.