Triple
T5399879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montu |
E120745
|
entity |
| Predicate | inversionType |
P63750
|
FINISHED |
| Object | vertical loop |
—
|
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: vertical loop | Statement: [Montu, inversionType, vertical loop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inversionType Context triple: [Montu, inversionType, vertical loop]
-
A.
investmentType
Indicates the specific category or nature of an investment associated with an entity or transaction.
-
B.
assetType
Indicates the specific category or classification of an asset within a broader asset framework or system.
-
C.
vestmentStyle
Indicates the style or type of clothing or ceremonial garments associated with an entity.
-
D.
investsIn
Indicates that one entity allocates resources, typically money or capital, into another entity with the expectation of future returns or benefits.
-
E.
inventionType
Indicates the specific category or kind of invention that characterizes the relationship between an invention and its type.
- 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_69bd4637b92c8190b815b6443ae4b323 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8932b8bc8190bd31e11b167a7212 |
completed | March 20, 2026, 5:51 p.m. |
| PD | Predicate disambiguation | batch_69bd84660ea08190a641084814fcf94d |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd8931302c81908afcb0f011e91f09 |
completed | March 20, 2026, 5:51 p.m. |
Created at: March 20, 2026, 2:04 p.m.