Triple
T5452232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giga Texas |
E122394
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Tesla, Inc. |
E3005
|
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: Tesla, Inc. | Statement: [Giga Texas, employer, Tesla, Inc.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tesla, Inc. Context triple: [Giga Texas, employer, Tesla, Inc.]
-
A.
Tesla, Inc.
chosen
Tesla, Inc. is an American electric vehicle and clean energy company known for pioneering mass-market electric cars, advanced battery technology, and autonomous driving innovations.
-
B.
Tesla
Tesla is a renowned Serbian-American inventor, electrical engineer, and futurist best known for his pioneering work on alternating current (AC) power systems and numerous innovations in electromagnetism.
-
C.
Lucid Group
Lucid Group is an American electric vehicle manufacturer best known for its luxury, high-performance EVs that compete with brands like Tesla and Mercedes-Benz.
-
D.
Li Auto
Li Auto is a Chinese electric vehicle manufacturer known for its extended-range hybrid SUVs and focus on smart, family-oriented cars.
-
E.
Tesla Semi
The Tesla Semi is an all-electric Class 8 semi-truck designed to offer high efficiency, long range, and lower operating costs compared to traditional diesel trucks.
- 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_69bd46424248819085282ddf50a565f3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd91dfec248190af6f9c793a99c34c |
completed | March 20, 2026, 6:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf77b24ac081908bae47b74c53eef0 |
completed | March 22, 2026, 5:01 a.m. |
Created at: March 20, 2026, 2:08 p.m.