Triple
T11319343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meg Tilly |
E268050
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Chan |
E14920
|
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: Chan | Statement: [Meg Tilly, familyName, Chan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chan Context triple: [Meg Tilly, familyName, Chan]
-
A.
Chan
chosen
Chan is a common Chinese surname shared by many notable individuals across various fields worldwide.
-
B.
Cha
Cha is the Korean family name of Theresa Hak Kyung Cha, the avant-garde artist and writer best known for her experimental book "Dictee."
-
C.
Cho
Cho is a common Korean surname borne by numerous notable individuals across entertainment, politics, sports, and other fields.
-
D.
CHAN
CHAN is the commonly used acronym for the African Nations Championship, a continental football tournament featuring national teams composed exclusively of players active in their domestic leagues.
-
E.
Chen
Chen is a common Chinese surname borne by many notable individuals across politics, arts, science, and technology.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9de875481908acfa56015d4b46f |
completed | April 9, 2026, 6:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e525e2549081909ec99e4c7006fd66 |
completed | April 19, 2026, 6:58 p.m. |
Created at: April 8, 2026, 9:32 p.m.