Triple
T197516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fitbit |
E4030
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object | FIT |
E4030
|
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: FIT | Statement: [Fitbit, tickerSymbol, FIT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FIT Context triple: [Fitbit, tickerSymbol, FIT]
-
A.
F
F is the New York Stock Exchange ticker symbol for Ford Motor Company, the American multinational automaker known for mass-producing automobiles and pioneering assembly line manufacturing.
-
B.
Flo
Flo was one of Jane Goodall’s most famous Gombe chimpanzees, known as a highly influential matriarch whose life and family were central to long-term studies of chimpanzee behavior and social structure.
-
C.
FÜ
FÜ is the vehicle registration code used on license plates for the city of Fürth in Bavaria, Germany.
-
D.
WHR
WHR is the commonly used abbreviation for the World Health Report, the World Health Organization’s flagship publication on global health statistics, trends, and policy.
-
E.
Fitbit
chosen
Fitbit is a consumer electronics and fitness company best known for its wearable activity trackers and smartwatches that monitor health and exercise metrics.
- 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25bc96aa081908ef74c9827c9aa48 |
completed | Feb. 28, 2026, 3:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3161ddd388190954fd4b73b32a3b1 |
completed | Feb. 28, 2026, 4:21 p.m. |
Created at: Feb. 28, 2026, 2:44 a.m.