Triple
T4051141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TicPods |
E84587
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Mobvoi |
E14245
|
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: Mobvoi | Statement: [TicPods, brand, Mobvoi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mobvoi Context triple: [TicPods, brand, Mobvoi]
-
A.
Mobvoi
chosen
Mobvoi is a Chinese artificial intelligence company best known for its TicWatch line of smartwatches and other wearable devices.
-
B.
Mobvoi TicWatch Pro
The Mobvoi TicWatch Pro is a Wear OS-powered smartwatch known for its dual-layer display that extends battery life while offering full smartwatch functionality.
-
C.
Xiaomi
Xiaomi is a major Chinese electronics and smartphone manufacturer known for its affordable, feature-rich devices and rapidly growing global presence.
-
D.
XPeng
XPeng is a Chinese electric vehicle manufacturer known for its smart, tech-focused cars and advanced driver-assistance systems.
-
E.
Li Auto
Li Auto is a Chinese electric vehicle manufacturer known for its extended-range hybrid SUVs and focus on smart, family-oriented cars.
- 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_69aed933bec881909edfa28ebb69c634 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb8539148190990468c1429be9dd |
completed | March 9, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b501c008190ac115240328c6fc9 |
completed | March 14, 2026, 2:06 p.m. |
Created at: March 9, 2026, 3:37 p.m.