Triple
T35930802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helena Skleraina |
E1039155
|
entity |
| Predicate | memberOfDynastyOrHouse |
P91745
|
FINISHED |
| Object |
Skleros family
The Skleros family was a prominent Byzantine aristocratic lineage that produced several influential military and political figures in the Middle Ages.
|
E2160949
|
NE FINISHED |
How this triple was built (3 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: Skleros family | Statement: [Helena Skleraina, memberOfDynastyOrHouse, Skleros family]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Skleros family Triple: [Helena Skleraina, memberOfDynastyOrHouse, Skleros family]
Generated description
The Skleros family was a prominent Byzantine aristocratic lineage that produced several influential military and political figures in the Middle Ages.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: memberOfDynastyOrHouse Context triple: [Helena Skleraina, memberOfDynastyOrHouse, Skleros family]
-
A.
dynasticHouseInvolved
Indicates that a particular dynastic house (royal or noble lineage) is involved in, associated with, or plays a role in the referenced event, entity, or relationship.
-
B.
partOfDynasty
chosen
Indicates that an entity belongs to, or is a constituent member of, a particular dynasty.
-
C.
associatedWithDynasty
Indicates that an entity has a historical, political, cultural, or familial connection to a specific dynasty.
-
D.
associatedWithDynastyOrFaction
Indicates that an entity is linked to, belongs to, or is significantly connected with a particular dynasty or faction.
-
E.
ethnicBaseOfRulingHouse
Indicates that a particular ethnic group forms the primary ancestral or cultural basis of a ruling house or dynasty.
- F. None of above.
Provenance (6 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_69f76e23e4688190a5369138755138bf |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd7fdafbe881908a31fcb407af2c34 |
completed | May 8, 2026, 6:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a38ae3c70f48190b8b572d055f8eca6 |
completed | June 22, 2026, 3:38 a.m. |
| NEDg | Description generation | batch_6a38af026ee88190b64529c38d3568ec |
completed | June 22, 2026, 3:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a38af6392e881909e45170695c2dad1 |
completed | June 22, 2026, 3:43 a.m. |
| PD | Predicate disambiguation | batch_69fd7ef0ea908190b5d83f71565bdb1c |
completed | May 8, 2026, 6:13 a.m. |
Created at: May 3, 2026, 4:07 p.m.