Triple
T4019741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | O'Reilly Auto Parts |
E91250
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object |
ORLY
ORLY is the stock ticker symbol for O’Reilly Auto Parts, a major U.S. retailer and distributor of automotive aftermarket parts, tools, and accessories.
|
E405955
|
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: ORLY | Statement: [O'Reilly Auto Parts, tickerSymbol, ORLY]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ORLY Context triple: [O'Reilly Auto Parts, tickerSymbol, ORLY]
-
A.
Orly
Orly is a commune in the southern suburbs of Paris, France, best known for giving its name to the nearby Paris Orly Airport.
-
B.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
C.
Essie
Essie is a popular nail polish and nail care brand known for its wide range of fashion-forward colors and salon-quality formulas.
-
D.
Herbal Essences
Herbal Essences is a popular hair care brand known for its fragranced shampoos and conditioners often marketed with botanical and natural imagery.
-
E.
Kylie Cosmetics
Kylie Cosmetics is a makeup and beauty brand founded by Kylie Jenner, known for its trend-setting lip kits and social media–driven marketing.
- 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: ORLY Triple: [O'Reilly Auto Parts, tickerSymbol, ORLY]
Generated description
ORLY is the stock ticker symbol for O’Reilly Auto Parts, a major U.S. retailer and distributor of automotive aftermarket parts, tools, and accessories.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ORLY Target entity description: ORLY is the stock ticker symbol for O’Reilly Auto Parts, a major U.S. retailer and distributor of automotive aftermarket parts, tools, and accessories.
-
A.
Orly
Orly is a commune in the southern suburbs of Paris, France, best known for giving its name to the nearby Paris Orly Airport.
-
B.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
C.
Essie
Essie is a popular nail polish and nail care brand known for its wide range of fashion-forward colors and salon-quality formulas.
-
D.
Herbal Essences
Herbal Essences is a popular hair care brand known for its fragranced shampoos and conditioners often marketed with botanical and natural imagery.
-
E.
Kylie Cosmetics
Kylie Cosmetics is a makeup and beauty brand founded by Kylie Jenner, known for its trend-setting lip kits and social media–driven marketing.
- 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_69aed9618b04819081750d979d2af098 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaab39d4819080e37cd175c89542 |
completed | March 9, 2026, 4:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c7cb0ec8190b7e4646a934ddb07 |
completed | March 14, 2026, 11:54 a.m. |
| NEDg | Description generation | batch_69b54d06c7c881908b30ba813c009d25 |
completed | March 14, 2026, 11:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b54d739e588190b23c06b10b4f540e |
completed | March 14, 2026, 11:58 a.m. |
Created at: March 9, 2026, 3:35 p.m.