Triple
T9577533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babar languages |
E231081
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Marsela language
The Marsela language is an Austronesian language spoken on Marsela Island in the Maluku province of Indonesia.
|
E809234
|
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: Marsela language | Statement: [Babar languages, hasMember, Marsela language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marsela language Context triple: [Babar languages, hasMember, Marsela language]
-
A.
Mararit language
The Mararit language is a lesser-known Nilo-Saharan language spoken by the Mararit people in parts of Chad and Sudan.
-
B.
Maru language
The Maru language is a Sino-Tibetan language spoken primarily by the Maru (Lawngwaw) people in parts of Myanmar and neighboring regions.
-
C.
Damara language
The Damara language is a Khoe (Central Khoisan) language spoken primarily by the Damara people of Namibia.
-
D.
Lasgerdi language
The Lasgerdi language is an Iranian language spoken in parts of north-central Iran and classified within the Semnani branch of Northwestern Iranian languages.
-
E.
Patelia language
The Patelia language is a regional Indo-Aryan tribal language variety associated with the Bhil communities of western India.
- 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: Marsela language Triple: [Babar languages, hasMember, Marsela language]
Generated description
The Marsela language is an Austronesian language spoken on Marsela Island in the Maluku province of Indonesia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marsela language Target entity description: The Marsela language is an Austronesian language spoken on Marsela Island in the Maluku province of Indonesia.
-
A.
Mararit language
The Mararit language is a lesser-known Nilo-Saharan language spoken by the Mararit people in parts of Chad and Sudan.
-
B.
Maru language
The Maru language is a Sino-Tibetan language spoken primarily by the Maru (Lawngwaw) people in parts of Myanmar and neighboring regions.
-
C.
Damara language
The Damara language is a Khoe (Central Khoisan) language spoken primarily by the Damara people of Namibia.
-
D.
Lasgerdi language
The Lasgerdi language is an Iranian language spoken in parts of north-central Iran and classified within the Semnani branch of Northwestern Iranian languages.
-
E.
Patelia language
The Patelia language is a regional Indo-Aryan tribal language variety associated with the Bhil communities of western India.
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99ad7d108190a0b8c975351ea727 |
completed | April 1, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d16155b3288190ac135c3a1e58cc7e |
completed | April 4, 2026, 7:07 p.m. |
| NEDg | Description generation | batch_69d161e6a1308190932c8386e1c24f2e |
completed | April 4, 2026, 7:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d165a8c80081909e4d0837cbaabf95 |
completed | April 4, 2026, 7:25 p.m. |
Created at: March 30, 2026, 8:05 p.m.