Triple
T15696173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Dalassene |
E380464
|
entity |
| Predicate | heldTitle |
P8
|
FINISHED |
| Object |
sebaste
Sebaste was a prestigious Byzantine court title, often granted to high-ranking women of the imperial family to signify their elevated status and influence.
|
E1170476
|
NE FINISHED |
How this triple was built (4 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: sebaste | Statement: [Anna Dalassene, heldTitle, sebaste]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: sebaste Context triple: [Anna Dalassene, heldTitle, sebaste]
-
A.
SEBL
SEBL was the stock ticker symbol for Siebel Systems, a prominent customer relationship management (CRM) software company later acquired by Oracle.
-
B.
SEB
SEB is a major Swedish financial group and bank, historically linked to the influential Wallenberg family and known for its corporate and investment banking services in Northern Europe.
-
C.
SEB
SEB is the IATA airport code for Sabha Airport, which serves the city of Sabha in southwestern Libya.
-
D.
SEST
SEST is the ICAO airport code for San Cristóbal Airport, which serves San Cristóbal Island in the Galápagos, Ecuador.
-
E.
SEREB
SEREB was a French aerospace company involved in the development of ballistic missiles and space launch vehicles before being merged into Aérospatiale.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: sebaste Triple: [Anna Dalassene, heldTitle, sebaste]
Generated description
Sebaste was a prestigious Byzantine court title, often granted to high-ranking women of the imperial family to signify their elevated status and influence.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: sebaste Target entity description: Sebaste was a prestigious Byzantine court title, often granted to high-ranking women of the imperial family to signify their elevated status and influence.
-
A.
SEBL
SEBL was the stock ticker symbol for Siebel Systems, a prominent customer relationship management (CRM) software company later acquired by Oracle.
-
B.
SEB
SEB is a major Swedish financial group and bank, historically linked to the influential Wallenberg family and known for its corporate and investment banking services in Northern Europe.
-
C.
SEB
SEB is the IATA airport code for Sabha Airport, which serves the city of Sabha in southwestern Libya.
-
D.
SEST
SEST is the ICAO airport code for San Cristóbal Airport, which serves San Cristóbal Island in the Galápagos, Ecuador.
-
E.
SEREB
SEREB was a French aerospace company involved in the development of ballistic missiles and space launch vehicles before being merged into Aérospatiale.
- F. None of above. chosen
Provenance (5 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f50ce848190a839c4fb7306d793 |
completed | April 16, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6eeeef188190a70aca06ddfe11e6 |
completed | May 9, 2026, 5:29 p.m. |
| NEDg | Description generation | batch_69ff6f4c9a008190895fee7abeb62536 |
completed | May 9, 2026, 5:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff6fa74b1c8190a7ceb63943639793 |
completed | May 9, 2026, 5:32 p.m. |
Created at: April 10, 2026, 4:44 a.m.