Triple
T30405492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mach-O binary format |
E773465
|
entity |
| Predicate | fatFilePurpose |
P169752
|
FINISHED |
| Object | store multiple architectures in one file |
—
|
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: store multiple architectures in one file | Statement: [Mach-O binary format, fatFilePurpose, store multiple architectures in one file]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fatFilePurpose Context triple: [Mach-O binary format, fatFilePurpose, store multiple architectures in one file]
-
A.
fatFileAlsoCalled
Indicates that a FAT (File Allocation Table) file is known or referred to by an alternative name or alias.
-
B.
fatStorage
Indicates the process or state in which an organism or system accumulates and retains fat as an energy reserve.
-
C.
fatDistribution
Indicates how body fat is spatially allocated or spread across different regions of an entity.
-
D.
furUse
Indicates that one entity uses the fur of another entity, typically as material or resource.
-
E.
fatColor
Indicates the color characteristic associated with an entity’s fat or fatty tissue.
- 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_69f2248facd48190b183c3f3ca6daef7 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6861d69a08190802564e3aa7d6ea7 |
completed | May 2, 2026, 11:17 p.m. |
| PD | Predicate disambiguation | batch_69f67e40af9881908de3a4aa15f70a83 |
completed | May 2, 2026, 10:44 p.m. |
| PDg | Predicate description generation | batch_69f67f7e116c819099aec724e9ef3763 |
completed | May 2, 2026, 10:49 p.m. |
Created at: April 29, 2026, 8:03 p.m.