Triple
T5503616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mao Anying |
E144384
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Anying |
E144384
|
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: Anying | Statement: [Mao Anying, givenName, Anying]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anying Context triple: [Mao Anying, givenName, Anying]
-
A.
Anying
chosen
Anying is the given name of Mao Anying, the eldest son of Chinese leader Mao Zedong who was killed in action during the Korean War.
-
B.
Anyanya
Anyanya was a southern Sudanese separatist rebel movement that fought for independence from the northern-dominated government during the First Sudanese Civil War.
-
C.
Anini
Anini is a remote town in the Dibang Valley district of Arunachal Pradesh in northeastern India, known for its rugged Himalayan terrain and proximity to the Dibang River.
-
D.
Anya
Anya is the given name of actress Anya Taylor-Joy, known for her roles in films like "The Witch" and the series "The Queen's Gambit."
-
E.
Anya
Anya is a person known primarily through her relationship to someone named Hannah, likely as a friend or family member.
- 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f0bbea48190bb6fecaee9c0b1d0 |
completed | March 22, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027aa65608190a89fdfb0da675d4d |
completed | March 22, 2026, 5:32 p.m. |
Created at: March 22, 2026, 3:32 p.m.