Triple
T22110164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SIP |
E546390
|
entity |
| Predicate | containsHistoricCharacters |
P147021
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [SIP, containsHistoricCharacters, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsHistoricCharacters Context triple: [SIP, containsHistoricCharacters, true]
-
A.
hasHumanCharacters
Indicates that the subject includes or features characters that are human beings.
-
B.
hasHistoricity
Indicates that something possesses historical existence, significance, or authenticity, rather than being purely fictional, mythical, or timeless.
-
C.
hasHistoricalText
Indicates that an entity is associated with a historical written document or record describing it or its past.
-
D.
historicalCharacteristic
Indicates that an entity possesses a trait, feature, or quality that is rooted in or defined by its history or past events.
-
E.
isHistoric
Indicates that something has significant importance or relevance in history, often due to its age, impact, or role in past events.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12948c2ec819083340787b2062649 |
completed | April 28, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69e71b2ed7348190b6fa2e52f54393fb |
completed | April 21, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e7222d208c819098b12c13e31af629 |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 8:30 p.m.