Triple
T38623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elon Musk |
E764
|
entity |
| Predicate | coFounded |
P104
|
FINISHED |
| Object |
Zip2
Zip2 was an early online city guide and business directory software company from the late 1990s that provided web-based publishing tools for newspapers.
|
E3340
|
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: Zip2 | Statement: [Elon Musk, coFounded, Zip2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zip2 Context triple: [Elon Musk, coFounded, Zip2]
-
A.
Blitz
The Blitz was the sustained German bombing campaign against the United Kingdom, particularly London, during 1940–1941 in World War II.
-
B.
Metro
Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
-
C.
ARC
ARC is the commonly used acronym for the Augmentation Research Center, a pioneering research group known for its early work on interactive computing and human–computer interaction.
-
D.
HUP
HUP is a major academic medical center in Philadelphia that serves as the flagship teaching hospital of the University of Pennsylvania's health system.
-
E.
Ventra
Ventra is the contactless fare payment system used across Chicago’s public transit network, including buses and trains.
- 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: Zip2 Triple: [Elon Musk, coFounded, Zip2]
Generated description
Zip2 was an early online city guide and business directory software company from the late 1990s that provided web-based publishing tools for newspapers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zip2 Target entity description: Zip2 was an early online city guide and business directory software company from the late 1990s that provided web-based publishing tools for newspapers.
-
A.
A Loop
A Loop is a modern streetcar route in Portland, Oregon, that provides circulator service through the central city and adjacent neighborhoods as part of the Portland Streetcar system.
-
B.
Blitz
The Blitz was the sustained German bombing campaign against the United Kingdom, particularly London, during 1940–1941 in World War II.
-
C.
DELTA
DELTA is the radio callsign used by pilots and air traffic control to identify and communicate with Delta Air Lines flights.
-
D.
Metro
Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
-
E.
ARC
ARC is the commonly used acronym for the Augmentation Research Center, a pioneering research group known for its early work on interactive computing and human–computer interaction.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24acd14b48190b80d4329621583df |
completed | Feb. 28, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a24e6222408190bc317b90aea16849 |
completed | Feb. 28, 2026, 2:09 a.m. |
| NEDg | Description generation | batch_69a250f6c0308190affd58f1bfa0c261 |
completed | Feb. 28, 2026, 2:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a251b471688190b067c5db8f03ac47 |
completed | Feb. 28, 2026, 2:23 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.