Triple
T6094274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TI-86 |
E135838
|
entity |
| Predicate | hasROM |
P68075
|
FINISHED |
| Object | 256 KB |
—
|
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: 256 KB | Statement: [TI-86, hasROM, 256 KB]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasROM Context triple: [TI-86, hasROM, 256 KB]
-
A.
hasRAM
Indicates that an entity possesses or is equipped with a specified amount or type of random-access memory (RAM).
-
B.
hasMP
Indicates that an entity is represented by, or associated with, a specific Member of Parliament (MP).
-
C.
hasCP
Indicates that an entity possesses, is associated with, or is characterized by a specific CP (such as a control point, contact person, or configuration parameter), depending on the domain context.
-
D.
hasHardwareCompatibilityWith
Indicates that two hardware components or systems can operate together correctly and reliably without conflicts or incompatibilities.
-
E.
hasRomanizationOf
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05a9516ec819093e94ee8d3244e1b |
completed | March 22, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c049f3b1ec8190bea67a7bec6442a5 |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8e3f2c8190be459ca02f9b315a |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:12 p.m.