Triple
T36107193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mito phase |
E1044391
|
entity |
| Predicate | languageOfScholarlyDescription |
P108236
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [Mito phase, languageOfScholarlyDescription, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfScholarlyDescription Context triple: [Mito phase, languageOfScholarlyDescription, Spanish]
-
A.
languageOfManuscript
Indicates the language in which a given manuscript is written.
-
B.
languageOfOriginalDescription
chosen
Indicates that something is expressed or documented in its initial or source language version.
-
C.
primaryLanguageOfCataloguing
Indicates the language primarily used to create or record the entries in a catalog.
-
D.
languageOfInstitutionalContext
Indicates the language used as the primary medium of communication within an institutional setting or context.
-
E.
languageOfAwardingInstitution
Indicates the language in which the awarding institution formally grants or documents the award.
- 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_69f76e338e2c8190b7f3bc68bec76349 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a00dc330b148190aaae2ac6a5327960 |
completed | May 10, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_6a00d9d2904881909dafbfe7b9e5ad81 |
completed | May 10, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:08 p.m.