Triple
T199008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ford Model T |
E4060
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Tin Lizzie
Tin Lizzie is the popular nickname for the Ford Model T, the early 20th-century automobile that revolutionized mass car production and personal transportation.
|
E25545
|
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: Tin Lizzie | Statement: [Ford Model T, alsoKnownAs, Tin Lizzie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tin Lizzie Context triple: [Ford Model T, alsoKnownAs, Tin Lizzie]
-
A.
Excelsior
Excelsior is the Latin state motto of New York, meaning "ever upward" and symbolizing aspiration and continual progress.
-
B.
Thomas Tinker
Thomas Tinker was an English Separatist and early Pilgrim who sailed on the Mayflower and died during the first harsh winter at Plymouth Colony.
-
C.
Earl
An Earl is a noble rank in the British and some European peerage systems, historically positioned below a marquess and above a viscount.
-
D.
Horseshoe
Horseshoe is a well-known casino and racetrack brand in the United States, recognized for its gambling, entertainment, and hospitality offerings.
-
E.
Tessie
Tessie is a Boston Red Sox mascot character, often depicted as a green monster and associated with Wally the Green Monster.
- 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: Tin Lizzie Triple: [Ford Model T, alsoKnownAs, Tin Lizzie]
Generated description
Tin Lizzie is the popular nickname for the Ford Model T, the early 20th-century automobile that revolutionized mass car production and personal transportation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tin Lizzie Target entity description: Tin Lizzie is the popular nickname for the Ford Model T, the early 20th-century automobile that revolutionized mass car production and personal transportation.
-
A.
Excelsior
Excelsior is the Latin state motto of New York, meaning "ever upward" and symbolizing aspiration and continual progress.
-
B.
Thomas Tinker
Thomas Tinker was an English Separatist and early Pilgrim who sailed on the Mayflower and died during the first harsh winter at Plymouth Colony.
-
C.
Earl
An Earl is a noble rank in the British and some European peerage systems, historically positioned below a marquess and above a viscount.
-
D.
Horseshoe
Horseshoe is a well-known casino and racetrack brand in the United States, recognized for its gambling, entertainment, and hospitality offerings.
-
E.
Tessie
Tessie is a Boston Red Sox mascot character, often depicted as a green monster and associated with Wally the Green Monster.
- 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25bcb2c7c8190b0e031e93651182a |
completed | Feb. 28, 2026, 3:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a31c93aa348190a7555a8327f7ad99 |
completed | Feb. 28, 2026, 4:49 p.m. |
| NEDg | Description generation | batch_69a320abcce08190867d01cd84a0a632 |
completed | Feb. 28, 2026, 5:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a321016748819090356a3369138d21 |
completed | Feb. 28, 2026, 5:08 p.m. |
Created at: Feb. 28, 2026, 2:44 a.m.