Triple
T21544380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marale |
E531580
|
entity |
| Predicate | hasOfficialName |
P66
|
FINISHED |
| Object | Marale |
—
|
NE NERFINISHED |
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: Marale | Statement: [Marale, hasOfficialName, Marale]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marale Context triple: [Marale, hasOfficialName, Marale]
-
A.
Marale
chosen
Marale is a small municipality located in the Francisco Morazán Department of central Honduras.
-
B.
Maralal
Maralal is a remote market town in northern Kenya known as a gateway to the arid north and as a traditional center for the Samburu people.
-
C.
Maralik
Maralik is a small town in northwestern Armenia known for its agricultural surroundings and location within the Shirak region.
-
D.
Marassi
Marassi is a residential district of Genoa, Italy, best known for hosting the historic Stadio Luigi Ferraris football stadium.
-
E.
Marcali
Marcali is a small town in southwestern Hungary known for its agricultural surroundings and role as a local administrative and service center in Somogy County.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c45f17148190949c330ab9c27706 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeb58d66ec8190b654a46932c841d3 |
completed | April 27, 2026, 1:02 a.m. |
Created at: April 16, 2026, 6:28 p.m.