Triple
T15181564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tinée |
E362757
|
entity |
| Predicate | hasFrenchName |
P744
|
FINISHED |
| Object | Tinée |
E362757
|
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: Tinée | Statement: [Tinée, hasFrenchName, Tinée]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tinée Context triple: [Tinée, hasFrenchName, Tinée]
-
A.
Tinée
chosen
Tinée is a river in southeastern France that flows through the Alpes-Maritimes department in the Provence-Alpes-Côte d'Azur region.
-
B.
Ta’aisha
The Ta’aisha are a Sudanese Arab tribal group from the Darfur–Kordofan region, historically prominent through their leadership role in the Mahdist state under Abdallahi ibn Muhammad.
-
C.
Tineg
Tineg is a remote, mountainous municipality in the province of Abra in the Philippines, known for its rugged terrain and largely rural, forested landscape.
-
D.
Tifanie
Tifanie is a given name, typically a feminine variant of the name Tiffany.
-
E.
Teni
Teni is a Nigerian singer-songwriter and entertainer known for her catchy Afropop hits and playful, charismatic style.
- 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006663ad48190986b680001be0e9b |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec89210e081909e8077fa2487c40e |
completed | May 9, 2026, 5:39 a.m. |
Created at: April 10, 2026, 3:09 a.m.