Triple
T460314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British American Tobacco |
E7321
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
BAT
BAT is a major multinational tobacco company known for producing and marketing cigarettes and other nicotine products worldwide.
|
E57781
|
NE FINISHED |
How this triple was built (4 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: BAT | Statement: [British American Tobacco, alsoKnownAs, BAT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BAT Context triple: [British American Tobacco, alsoKnownAs, BAT]
-
A.
BAL
BAL is the Amtrak station code for Pennsylvania Station in Baltimore, Maryland, a major rail hub in the city’s transportation network.
-
B.
BA
BA is the New York Stock Exchange ticker symbol for The Boeing Company, a major American aerospace and defense manufacturer.
-
C.
BO
BO is the two-letter ISO 3166-1 alpha-2 country code that uniquely identifies Bolivia in international standards and systems.
-
D.
ATA
ATA (Advanced Technology Attachment), commonly known as IDE, is a standard interface used to connect storage devices like hard drives and optical drives to a computer's motherboard.
-
E.
BEL
BEL is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies Belgium in international standards and data systems.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: BAT Triple: [British American Tobacco, alsoKnownAs, BAT]
Generated description
BAT is a major multinational tobacco company known for producing and marketing cigarettes and other nicotine products worldwide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: BAT Target entity description: BAT is a major multinational tobacco company known for producing and marketing cigarettes and other nicotine products worldwide.
-
A.
BAL
BAL is the Amtrak station code for Pennsylvania Station in Baltimore, Maryland, a major rail hub in the city’s transportation network.
-
B.
BA
BA is the New York Stock Exchange ticker symbol for The Boeing Company, a major American aerospace and defense manufacturer.
-
C.
BO
BO is the two-letter ISO 3166-1 alpha-2 country code that uniquely identifies Bolivia in international standards and systems.
-
D.
ATA
ATA (Advanced Technology Attachment), commonly known as IDE, is a standard interface used to connect storage devices like hard drives and optical drives to a computer's motherboard.
-
E.
BEL
BEL is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies Belgium in international standards and data systems.
- F. None of above. chosen
Provenance (5 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_69a2e7e5c5bc8190a1dc8178218fba40 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2efbd6ed481909ec40f12b5b675c8 |
completed | Feb. 28, 2026, 1:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a44f583ea081908d92fe5b5dc4d3c0 |
completed | March 1, 2026, 2:38 p.m. |
| NEDg | Description generation | batch_69a45150b3f8819094519329a68fb1b8 |
completed | March 1, 2026, 2:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a451ab785c8190b4cab0d162b4efa8 |
completed | March 1, 2026, 2:48 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.