Triple
T2792593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GNU Tar |
E61962
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | tar |
E61962
|
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: tar | Statement: [GNU Tar, alsoKnownAs, tar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: tar Context triple: [GNU Tar, alsoKnownAs, tar]
-
A.
TAR
TAR is the commonly used abbreviation for the Intergovernmental Panel on Climate Change’s Third Assessment Report on climate change.
-
B.
TAR
TAR is the ICAO airline designator assigned to Tunisair, the national flag carrier of Tunisia.
-
C.
TAR
TAR is a Mexican regional airline operating domestic passenger flights to various destinations across the country.
-
D.
GNU Tar
chosen
GNU Tar is a widely used free software utility for creating, maintaining, modifying, and extracting files from archive files, especially on Unix-like systems.
-
E.
Engrampa archive manager
Engrampa archive manager is the MATE desktop environment’s file archiving tool used to create, view, and extract compressed archives in various formats.
- 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_69afc65ebe788190859012e930918b05 |
completed | March 10, 2026, 7:21 a.m. |
Created at: March 6, 2026, 9:58 p.m.