Triple
T1869996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macintosh LC |
E39011
|
entity |
| Predicate | codename |
P2980
|
FINISHED |
| Object |
Elsie
Elsie is the internal codename Apple used for the Macintosh LC personal computer during its development.
|
E208097
|
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: Elsie | Statement: [Macintosh LC, codename, Elsie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elsie Context triple: [Macintosh LC, codename, Elsie]
-
A.
Elsie
Elsie is a fictional character from the post-apocalyptic virtual reality game "After the Fall."
-
B.
Mollie
Mollie is a young girl in Enid Blyton's "The Wishing-Chair" series who, along with her brother Peter, goes on magical adventures using a flying wishing-chair.
-
C.
Martha
Martha is a feminine given name of Aramaic origin, historically borne by notable figures such as Martha Washington, the first First Lady of the United States.
-
D.
Phyllis
Phyllis is a 1970s American television sitcom, spun off from The Mary Tyler Moore Show, that stars Cloris Leachman as the widowed Phyllis Lindstrom starting a new life in San Francisco.
-
E.
Betsy
Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
- 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: Elsie Triple: [Macintosh LC, codename, Elsie]
Generated description
Elsie is the internal codename Apple used for the Macintosh LC personal computer during its development.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elsie Target entity description: Elsie is the internal codename Apple used for the Macintosh LC personal computer during its development.
-
A.
Elsie
Elsie is a fictional character from the post-apocalyptic virtual reality game "After the Fall."
-
B.
Mollie
Mollie is a young girl in Enid Blyton's "The Wishing-Chair" series who, along with her brother Peter, goes on magical adventures using a flying wishing-chair.
-
C.
Martha
Martha is a feminine given name of Aramaic origin, historically borne by notable figures such as Martha Washington, the first First Lady of the United States.
-
D.
Phyllis
Phyllis is a 1970s American television sitcom, spun off from The Mary Tyler Moore Show, that stars Cloris Leachman as the widowed Phyllis Lindstrom starting a new life in San Francisco.
-
E.
Betsy
Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
- 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_69a8862f7074819096afe7fe65e179e9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb0b95c0c8190a37907755541f8c6 |
completed | March 7, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69add1dab2a481909adb0a3132348cee |
completed | March 8, 2026, 7:45 p.m. |
| NEDg | Description generation | batch_69add25c9c208190a576cf1123c0a2e6 |
completed | March 8, 2026, 7:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69add32c32b08190be6624eefa2fa386 |
completed | March 8, 2026, 7:51 p.m. |
Created at: March 4, 2026, 7:34 p.m.