Triple
T8108759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Google Reader |
E189290
|
entity |
| Predicate | hadMobileVersion |
P66849
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Google Reader, hadMobileVersion, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadMobileVersion Context triple: [Google Reader, hadMobileVersion, yes]
-
A.
mobileVariant
chosen
Indicates that one entity is a mobile-specific version or adaptation of another entity.
-
B.
hasApp
Indicates that an entity possesses, provides, or is associated with a particular application.
-
C.
hasMob
Indicates that an entity possesses, controls, or is associated with a particular mob or group of mobile agents.
-
D.
hasMIC
Indicates that an entity has a specified Minimum Inhibitory Concentration (MIC) value in relation to an antimicrobial agent.
-
E.
hasCellularComponent
Indicates that an entity possesses, includes, or is associated with a specific cellular component as part of its structure or organization.
- 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_69ca82b9d5848190a24672775d5c5011 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb42fbc57c81908c6be87bbc547085 |
completed | March 31, 2026, 3:43 a.m. |
| PD | Predicate disambiguation | batch_69cb04a2ed1c8190b73562321ad688bc |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:32 p.m.