Triple
T10275256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sayil |
E240947
|
entity |
| Predicate | connectedTo |
P37
|
FINISHED |
| Object | Labná |
E240948
|
NE 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: Labná | Statement: [Sayil, connectedTo, Labná]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Labná Context triple: [Sayil, connectedTo, Labná]
-
A.
Labná
chosen
Labná is a small ancient Maya archaeological site in Mexico’s Yucatán Peninsula, noted for its ornate Puuc-style architecture and iconic arched gateway.
-
B.
Zelníčková
Zelníčková is a Czech surname, notably borne by Ivana Marie Zelníčková, the Czech-American businesswoman and former wife of Donald Trump.
-
C.
Blatná
Blatná is a historic Czech town best known for its picturesque water castle and surrounding ponds in the South Bohemian countryside.
-
D.
Litavka
Litavka is a river in the Czech Republic that flows through the town of Beroun before joining the Berounka River.
-
E.
Vejprnice
Vejprnice is a municipality and village in the Plzeň Region of the Czech Republic, located just west of the city of Plzeň.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d28b6cd4819084a7a5c1893b5ad8 |
completed | April 7, 2026, 9:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71cf7f644819085dd687b286004ad |
completed | April 9, 2026, 3:28 a.m. |
Created at: April 6, 2026, 11:37 a.m.