Triple
T2603825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LS |
E58608
|
entity |
| Predicate | airlineCallsign |
P13478
|
FINISHED |
| Object | Channex |
E58610
|
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: Channex | Statement: [LS, airlineCallsign, Channex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Channex Context triple: [LS, airlineCallsign, Channex]
-
A.
CHANNEX
chosen
CHANNEX is the radio callsign used by the British low-cost airline Jet2.com during air traffic communications.
-
B.
Zihlkanal
Zihlkanal is a man-made canal in western Switzerland that drains water from Lake Neuchâtel toward the Aare River system.
-
C.
The Green Channel
The Green Channel was the original working name for the American premium cable television network HBO before it officially launched under its better-known title.
-
D.
Volacom
Volacom is a company founded by Tesla co-founder and battery technology pioneer JB Straubel, likely focused on advanced engineering and sustainable technology solutions.
-
E.
Chenek
Chenek is a small highland settlement in Ethiopia’s Simien Mountains, commonly used as a base for trekking and accessing the nearby peak of Ras Dashen.
- 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_69ab4ac3523881909679750c9f8c2dec |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8340eac819084eb1fe6f0ac0aa0 |
completed | March 7, 2026, 7:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af83d6daa4819087ee111e648ce71d |
completed | March 10, 2026, 2:37 a.m. |
Created at: March 6, 2026, 9:49 p.m.