Triple
T35249859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shibboleth incident |
E1018066
|
entity |
| Predicate | hasLanguageTestWord |
P200229
|
FINISHED |
| Object | shibboleth |
—
|
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: shibboleth | Statement: [Shibboleth incident, hasLanguageTestWord, shibboleth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageTestWord Context triple: [Shibboleth incident, hasLanguageTestWord, shibboleth]
-
A.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
B.
hasTermLanguage
Indicates that a given term is expressed or defined in a particular language.
-
C.
hasLanguageFormOf
Indicates that one entity is a specific linguistic form, expression, or realization of the language used by another entity.
-
D.
hasLanguageOfSide
Indicates that an entity uses or is associated with a particular language on a specific side or aspect (e.g., one side of a bilingual object or interface).
-
E.
hasLanguageInCountry
Indicates that a particular language is used or recognized within a specified country.
- 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_69f76de407d081909dfc3c419817ae93 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff7bc112088190851501fb2a16103d |
completed | May 9, 2026, 6:24 p.m. |
| PD | Predicate disambiguation | batch_69ff7b45507c81909753866ad733601a |
completed | May 9, 2026, 6:21 p.m. |
| PDg | Predicate description generation | batch_69ff7bbfa4b08190b11e84861706b349 |
completed | May 9, 2026, 6:23 p.m. |
Created at: May 3, 2026, 4:02 p.m.