Triple
T819544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret |
E17722
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Meg |
E98328
|
NE FINISHED |
How this triple was built (2 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: Meg | Statement: [Margaret, hasShortForm, Meg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meg Context triple: [Margaret, hasShortForm, Meg]
-
A.
Meg
chosen
Meg is a common diminutive form of the female given name Margaret, often used in English-speaking countries.
-
B.
Emily
Emily Warren Roebling was a pioneering 19th-century American engineer best known for her crucial role in overseeing the completion of the Brooklyn Bridge.
-
C.
Susan
Susan is the middle name of Olivia Susan Clemens.
-
D.
Emma
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
-
E.
Jennifer
Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab656418819091ecb09e7ede2825 |
completed | March 1, 2026, 9:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7928f8a808190aaf5f2a2f3ee676f |
completed | March 4, 2026, 2:01 a.m. |
Created at: March 1, 2026, 7:38 p.m.