Triple
T16281808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katherine Emmet |
E395281
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object | Emmet |
E1003625
|
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: Emmet | Statement: [Katherine Emmet, hasFamilyName, Emmet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emmet Context triple: [Katherine Emmet, hasFamilyName, Emmet]
-
A.
Emmett
Emmett is the eccentric time-traveling scientist from the Back to the Future film series, best known for inventing the DeLorean time machine.
-
B.
Emmett
chosen
Emmett is a given name of Irish origin commonly used as a masculine first name and sometimes as a surname.
-
C.
Emmett
Emmett is a hardened yet resourceful survivor in the post-apocalyptic horror film "A Quiet Place Part II," who becomes a reluctant protector and guide to the remaining Abbott family.
-
D.
Emmett Richmond
Emmett Richmond is a charming and supportive lawyer character best known as Elle Woods’s love interest in the "Legally Blonde" film series.
-
E.
Dante Hicks
Dante Hicks is a beleaguered convenience store clerk and the central everyman protagonist of Kevin Smith’s film "Clerks."
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24910c6b881909ae5cc0908dd8eb2 |
completed | April 17, 2026, 2:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017c6b72081908a21e5099f463b62 |
completed | May 10, 2026, 5:29 a.m. |
Created at: April 10, 2026, 5:05 a.m.