Triple
T11060510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serto script |
E261493
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Serṭo
Serṭo is a cursive style of the Syriac alphabet used primarily in West Syriac Christian liturgical and literary traditions.
|
E902478
|
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: Serṭo | Statement: [Serto script, alsoKnownAs, Serṭo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serṭo Context triple: [Serto script, alsoKnownAs, Serṭo]
-
A.
Osorio
Osorio is a Spanish-language surname borne by various notable individuals across sports, politics, and the arts.
-
B.
Calvero
Calvero is the aging, once-famous clown portrayed by Charlie Chaplin in the 1952 film "Limelight," struggling with obscurity and seeking redemption through helping a young dancer.
-
C.
Montojo
Montojo is a Spanish surname most notably associated with Admiral Patricio Montojo y Pasarón, a commander in the Spanish–American War.
-
D.
San-São
San-São is the traditional Brazilian football derby between São Paulo FC and Santos FC, known for its historic rivalries and memorable matches.
-
E.
Ferrera
Ferrera is a Spanish-origin surname most prominently associated with American actress and producer America Ferrera.
- 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: Serṭo Triple: [Serto script, alsoKnownAs, Serṭo]
Generated description
Serṭo is a cursive style of the Syriac alphabet used primarily in West Syriac Christian liturgical and literary traditions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Serṭo Target entity description: Serṭo is a cursive style of the Syriac alphabet used primarily in West Syriac Christian liturgical and literary traditions.
-
A.
Osorio
Osorio is a Spanish-language surname borne by various notable individuals across sports, politics, and the arts.
-
B.
Calvero
Calvero is the aging, once-famous clown portrayed by Charlie Chaplin in the 1952 film "Limelight," struggling with obscurity and seeking redemption through helping a young dancer.
-
C.
Montojo
Montojo is a Spanish surname most notably associated with Admiral Patricio Montojo y Pasarón, a commander in the Spanish–American War.
-
D.
San-São
San-São is the traditional Brazilian football derby between São Paulo FC and Santos FC, known for its historic rivalries and memorable matches.
-
E.
Ferrera
Ferrera is a Spanish-origin surname most prominently associated with American actress and producer America Ferrera.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798e991848190b07c2f48dae38681 |
completed | April 9, 2026, 12:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c887d2148190b19c91b6eb548494 |
completed | April 18, 2026, 6:08 p.m. |
| NEDg | Description generation | batch_69e3cadf271081908d2b794a4288892a |
completed | April 18, 2026, 6:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3cf08cf108190966b4abd0514a6ea |
completed | April 18, 2026, 6:35 p.m. |
Created at: April 8, 2026, 9:26 p.m.