Triple
T8066536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wu Zixu |
E188256
|
entity |
| Predicate | roleInBattleOfBoju |
P28183
|
FINISHED |
| Object | military strategist for the State of Wu |
—
|
LITERAL 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: military strategist for the State of Wu | Statement: [Wu Zixu, roleInBattleOfBoju, military strategist for the State of Wu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInBattleOfBoju Context triple: [Wu Zixu, roleInBattleOfBoju, military strategist for the State of Wu]
-
A.
notableBattleRole
chosen
Indicates the specific role or function an entity played in a notable or historically significant battle.
-
B.
roleInKoreanWar
Indicates the specific function, position, or involvement an entity had during the Korean War.
-
C.
strategicRoleDuringWar
Indicates that an entity served a particular strategic function or importance during a specific war or armed conflict.
-
D.
roleDuringSecondSinoJapaneseWar
Indicates the specific role, position, or function an entity held or performed during the Second Sino-Japanese War.
-
E.
associatedWithBattle
Indicates a relationship where an entity is connected or linked to a specific battle, such as by participation, relevance, or involvement.
- F. None of above.
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_69ca82b42674819086840efea12478e5 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3ff75d208190b7c53d2fe55878ac |
completed | March 31, 2026, 3:31 a.m. |
| PD | Predicate disambiguation | batch_69cb049cd51c8190bb3b0f503e42fa8d |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:26 p.m.