Triple
T16553692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biochemistry (textbook) |
E402135
|
entity |
| Predicate | hasGlossary |
P124038
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Biochemistry (textbook), hasGlossary, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGlossary Context triple: [Biochemistry (textbook), hasGlossary, yes]
-
A.
hasGlossesBy
Indicates a relationship where one entity provides or is associated with explanatory glosses or definitions for another entity.
-
B.
hasMultilingualGlosses
Indicates that an entity is associated with glosses or explanatory labels available in multiple languages.
-
C.
hasEnglishGloss
Indicates that one entity serves as the English-language gloss or explanatory translation for the other entity.
-
D.
hasGlossonym
Indicates a relationship where an entity is associated with the specific name or term used to refer to a language (its glossonym).
-
E.
hasApproximateNumberOfGlosses
Indicates that an entity is associated with an estimated or non-exact count of glosses (explanatory notes or definitions).
- 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e34fc737ac8190b755e2a39b6ef32b |
completed | April 18, 2026, 9:32 a.m. |
| PD | Predicate disambiguation | batch_69e296a47b7481909d9958158510c806 |
completed | April 17, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69e2d7f97e548190a474691a152bd8e8 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:15 a.m.