Triple
T19482891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TCL |
E487436
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | TCL |
—
|
NE NERFINISHED |
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: TCL | Statement: [TCL, abbreviation, TCL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TCL Context triple: [TCL, abbreviation, TCL]
-
A.
TCL
TCL is the FAA location identifier code for Tuscaloosa National Airport in Tuscaloosa, Alabama.
-
B.
TCL
TCL is the public transport network operator serving Lyon and its metropolitan area in France, managing buses, trams, and metro services.
-
C.
TCL Corporation
chosen
TCL Corporation is a major Chinese electronics company best known globally for manufacturing televisions and other consumer electronics.
-
D.
Hisense Group
Hisense Group is a Chinese multinational electronics and appliance manufacturer best known for its televisions, home appliances, and consumer electronics sold worldwide under the Hisense brand.
-
E.
LeEco
LeEco is a Chinese technology and entertainment conglomerate known for its streaming services, smartphones, smart TVs, and ambitious but troubled expansion into electric vehicles and global media.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6343c23308190a35f462f95338651 |
completed | April 20, 2026, 2:12 p.m. |
Created at: April 10, 2026, 1:39 p.m.