Triple
T25173186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Pants |
E630376
|
entity |
| Predicate | isPopularNicknameIn |
P157990
|
FINISHED |
| Object | Beijing |
—
|
NE NERFINISHED |
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: Beijing | Statement: [Big Pants, isPopularNicknameIn, Beijing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isPopularNicknameIn Context triple: [Big Pants, isPopularNicknameIn, Beijing]
-
A.
isNickname
Indicates that one name is an informal or alternative name commonly used to refer to the same person or entity as another name.
-
B.
isOfficialNicknameOf
Indicates that one name is the formally recognized nickname or informal moniker used to refer to another entity.
-
C.
hasAffectionateNicknameFor
Indicates that one entity uses or assigns a fond, affectionate, or endearing nickname to another entity.
-
D.
isReferencedInNickname
Indicates that one entity is mentioned or alluded to within another entity’s nickname.
-
E.
usesNicknameOf
Indicates that one entity refers to another entity by a nickname derived from or associated with that other entity’s name.
- F. None of above. chosen
Provenance (4 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_69e75a87c9b88190ab60731902a99750 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f46d485b388190b7b45e9b0c4a9e15 |
completed | May 1, 2026, 9:07 a.m. |
| PD | Predicate disambiguation | batch_69f44d8043b081908bbffd7f044b4f26 |
completed | May 1, 2026, 6:51 a.m. |
| PDg | Predicate description generation | batch_69f45300bd488190bb1d4160f5534ef6 |
completed | May 1, 2026, 7:15 a.m. |
Created at: April 21, 2026, 12:21 p.m.