Triple
T2826700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isabella Damon |
E54939
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Damon |
E200598
|
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: Damon | Statement: [Isabella Damon, familyName, Damon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Damon Context triple: [Isabella Damon, familyName, Damon]
-
A.
Damon
Damon is a character in the pastoral myth of Acis and Galatea, often portrayed as a shepherd whose role highlights themes of love, jealousy, and counsel.
-
B.
Damon
chosen
Damon is a surname most prominently associated with American actor, producer, and screenwriter Matt Damon and his family.
-
C.
Sean Patrick Duke
Sean Patrick Duke is the birth name of American actor Sean Astin, known for roles in films such as "The Goonies," "Rudy," and "The Lord of the Rings" trilogy.
-
D.
Will Parker
Will Parker is a cheerful, somewhat naive cowboy and rodeo performer who provides comic relief and romantic subplots in the classic Rodgers and Hammerstein musical "Oklahoma!".
-
E.
Jeffrey Dean
Jeffrey Dean is a prominent American computer scientist and software engineer best known for his influential work on large-scale distributed systems and infrastructure at Google.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde94ba848190b2c990936e07e6bb |
completed | March 7, 2026, 8:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afceb20c508190ba87e102ba250a00 |
completed | March 10, 2026, 7:56 a.m. |
Created at: March 6, 2026, 9:59 p.m.