Triple
T14339274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pan Jianwei |
E355550
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Pan |
E32339
|
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: Pan | Statement: [Pan Jianwei, familyName, Pan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pan Context triple: [Pan Jianwei, familyName, Pan]
-
A.
Pan
Pan is the rustic Greek god of shepherds, flocks, and wild nature, often depicted with goat-like features and associated with music and untamed wilderness.
-
B.
Pan
chosen
Pan is a genus of great apes that includes chimpanzees and bonobos, our closest living evolutionary relatives.
-
C.
Pan
Pan is a 1894 novel by Norwegian author Knut Hamsun, known for its lyrical portrayal of nature and its psychologically intense depiction of love and jealousy.
-
D.
Pag
Pag is a Croatian Adriatic island known for its barren, moonlike landscape, distinctive sheep’s milk cheese, and historic lace-making tradition.
-
E.
Panenka
Panenka is a famous football penalty-taking technique in which the kicker delicately chips the ball down the center of the goal, typically as the goalkeeper dives to one side.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8e8674c0819091dfbe9c50778c5e |
completed | April 14, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd469bc538819099ed5b7061cf140d |
completed | May 8, 2026, 2:12 a.m. |
Created at: April 10, 2026, 1:14 a.m.