Triple
T7931055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DocuSign |
E184188
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object |
DOCU
DOCU is the stock ticker symbol for DocuSign, a leading provider of electronic signature and digital agreement management solutions.
|
E697116
|
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: DOCU | Statement: [DocuSign, tickerSymbol, DOCU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DOCU Context triple: [DocuSign, tickerSymbol, DOCU]
-
A.
DOCO
DOCO is a mixed-use entertainment, shopping, and dining district in downtown Sacramento, California, adjacent to the Golden 1 Center.
-
B.
DOC
DOC is the commonly used abbreviation for the Division of Organic Chemistry, a professional organization focused on advancing research and education in organic chemistry.
-
C.
DOC
DOC is the commonly used abbreviation for the New York City Department of Correction, the agency responsible for operating the city’s jail system.
-
D.
DOC
DOC is New Zealand’s government agency responsible for conserving the country’s natural and historic heritage, including national parks, native species, and protected areas.
-
E.
DOCS
DOCS is the stock ticker symbol for Dr. Martens, the British footwear brand best known for its durable leather boots with air-cushioned soles.
- 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: DOCU Triple: [DocuSign, tickerSymbol, DOCU]
Generated description
DOCU is the stock ticker symbol for DocuSign, a leading provider of electronic signature and digital agreement management solutions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DOCU Target entity description: DOCU is the stock ticker symbol for DocuSign, a leading provider of electronic signature and digital agreement management solutions.
-
A.
DOCO
DOCO is a mixed-use entertainment, shopping, and dining district in downtown Sacramento, California, adjacent to the Golden 1 Center.
-
B.
DOC
DOC is the commonly used abbreviation for the New York City Department of Correction, the agency responsible for operating the city’s jail system.
-
C.
DOC
DOC is the commonly used abbreviation for the Division of Organic Chemistry, a professional organization focused on advancing research and education in organic chemistry.
-
D.
DOC
DOC is New Zealand’s government agency responsible for conserving the country’s natural and historic heritage, including national parks, native species, and protected areas.
-
E.
DOCS
DOCS is the stock ticker symbol for Dr. Martens, the British footwear brand best known for its durable leather boots with air-cushioned soles.
- 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_69ca8290c21c8190906a5ca6fe2b03c4 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3accc388819087065ebe7d5d9591 |
completed | March 31, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5c01602081908ea1af24785260ff |
completed | March 31, 2026, 5:30 a.m. |
| NEDg | Description generation | batch_69cb5f22f89c8190a98208bf096a2427 |
completed | March 31, 2026, 5:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cb76d2dff8819085ad9e10baad1537 |
completed | March 31, 2026, 7:25 a.m. |
Created at: March 30, 2026, 5:07 p.m.