Triple
T4390992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theano |
E99360
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | MILA |
E28783
|
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: MILA | Statement: [Theano, developer, MILA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MILA Context triple: [Theano, developer, MILA]
-
A.
Mila
chosen
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
B.
Mille
Mille is a French surname most notably borne by individuals such as Stéphane Mille.
-
C.
Milies
Milies is a traditional mountain village in Greece known for its stone architecture, rich cultural heritage, and scenic location on the slopes of Mount Pelion.
-
D.
Milyan
Milyan is an extinct Anatolian Indo-European language once spoken in southwestern Asia Minor, known primarily from a small corpus of inscriptions.
-
E.
Malika
Malika is a feminine given name of Arabic origin commonly used in various Muslim-majority and North African cultures.
- 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_69b3454f739481909ff6c28331f0c0b9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352843d7c8190929b94c94eaa63df |
completed | March 12, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e530428881908d125971263bd747 |
completed | March 14, 2026, 10:46 p.m. |
Created at: March 12, 2026, 11:19 p.m.