Triple
T2792786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GNU Findutils |
E61965
|
entity |
| Predicate | hasCommand |
P15534
|
FINISHED |
| Object | updatedb |
E299203
|
NE 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: updatedb | Statement: [GNU Findutils, hasCommand, updatedb]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: updatedb Context triple: [GNU Findutils, hasCommand, updatedb]
-
A.
updatedb
chosen
updatedb is a command-line utility that builds and maintains a searchable index of filesystem paths for use by the locate command.
-
B.
Dracut
Dracut is a town in northeastern Massachusetts, United States, known for its suburban character and proximity to the city of Lowell.
-
C.
Apt
Apt is a historic market town in southeastern France’s Vaucluse department, known for its candied fruit production and Provençal charm.
-
D.
FatKat
FatKat is an AI-driven hedge fund and investment firm co-founded by futurist and inventor Ray Kurzweil to apply machine learning to financial market prediction.
-
E.
Nepomuk
Nepomuk is a small historic town in the Plzeň Region of the Czech Republic, best known as the birthplace of Saint John of Nepomuk.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddd107ac81908eb1a6946834eee3 |
completed | March 7, 2026, 8:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce9069f481908c4a3e8814caf590 |
completed | March 10, 2026, 7:56 a.m. |
Created at: March 6, 2026, 9:58 p.m.