Triple
T28433571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linju |
E715201
|
entity |
| Predicate | associatedWithBattleType |
P203273
|
FINISHED |
| Object | siege and capture of Guan Yu |
—
|
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: siege and capture of Guan Yu | Statement: [Linju, associatedWithBattleType, siege and capture of Guan Yu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithBattleType Context triple: [Linju, associatedWithBattleType, siege and capture of Guan Yu]
-
A.
associatedWithBattle
Indicates a relationship where an entity is connected or linked to a specific battle, such as by participation, relevance, or involvement.
-
B.
hasBattleClassAssociation
Indicates an association between a battle and a classification or category that characterizes that battle.
-
C.
battleCryAssociated
Indicates a relationship where a specific battle cry is linked or attributed to a particular entity, such as a person, group, or event.
-
D.
battleRelatedTo
Indicates a relationship where one entity is connected to, associated with, or relevant to a particular battle or combat event.
-
E.
battlegroundType
Indicates the specific kind or category of environment in which a battle or conflict takes place.
- 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_69efd6b253888190b3c7222ed6a403a8 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_6a01487b73488190954eb5143e6f246e |
completed | May 11, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_6a0145210ae481908da59b02efdbc397 |
completed | May 11, 2026, 2:55 a.m. |
| PDg | Predicate description generation | batch_6a01487ac3608190946beee970e5559b |
completed | May 11, 2026, 3:09 a.m. |
Created at: April 28, 2026, 1:41 a.m.