Triple
T15032363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPP |
E378383
|
entity |
| Predicate | effectOnMachines |
P116464
|
FINISHED |
| Object | gives machines distinct personalities |
—
|
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: gives machines distinct personalities | Statement: [GPP, effectOnMachines, gives machines distinct personalities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnMachines Context triple: [GPP, effectOnMachines, gives machines distinct personalities]
-
A.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
-
B.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
-
C.
fuelEffect
Indicates the influence or impact that a given fuel has on a process, system, or outcome.
-
D.
effectOnOthers
Indicates the impact or influence that one entity’s actions, presence, or state has on other entities.
-
E.
effectOnShips
Indicates the impact or influence that one entity, event, or condition has on ships.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e3a7c8819081f26c2435c1bcb2 |
completed | April 15, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69de9a67cbc481909c19c2de57de4eb7 |
completed | April 14, 2026, 7:50 p.m. |
| PDg | Predicate description generation | batch_69deb1a88d588190996afa8e5b32b552 |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:59 a.m.