Triple
T1389380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauss–Bonnet theorem (early form) |
E29918
|
entity |
| Predicate | relatedArea |
P6979
|
FINISHED |
| Object | algebraic topology |
—
|
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: algebraic topology | Statement: [Gauss–Bonnet theorem (early form), relatedArea, algebraic topology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedArea Context triple: [Gauss–Bonnet theorem (early form), relatedArea, algebraic topology]
-
A.
relatedField
chosen
Indicates that one field, topic, or area of study is connected or relevant to another in subject matter or application.
-
B.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
C.
relatedService
Indicates that one service is connected or associated with another service in a relevant or dependent way.
-
D.
relatedDivision
Indicates that there is an organizational or structural association between two divisions, such as being counterparts, partners, or otherwise linked within a broader entity.
-
E.
relatedPlace
Indicates a relationship where one place is connected or associated with another place in a relevant or meaningful way.
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c35ce48c81909aaad7dfa2df63fa |
completed | March 1, 2026, 10:53 p.m. |
| PD | Predicate disambiguation | batch_69a4beffcf808190ab4cd0271257ce63 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.