Triple
T3215274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asher Angel |
E67379
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Asher |
E65911
|
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: Asher | Statement: [Asher Angel, givenName, Asher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Asher Context triple: [Asher Angel, givenName, Asher]
-
A.
Asher
chosen
Asher is a biblical figure, one of the twelve sons of Jacob and the ancestor of the Israelite Tribe of Asher.
-
B.
Jeremy Ashkenas
Jeremy Ashkenas is an American programmer and open-source developer best known for creating the CoffeeScript language and contributing to projects like Backbone.js and Underscore.js.
-
C.
Alec
Alec is the familiar nickname of Alec Douglas-Home, a British Conservative politician who served as Prime Minister of the United Kingdom from 1963 to 1964.
-
D.
Jared
Jared is a village located in Pakistan’s scenic Kaghan Valley, known for its mountainous landscapes and tourism.
-
E.
Jared
Jared is the given name of Jared Diamond, an American geographer, historian, and author best known for his Pulitzer Prize–winning book "Guns, Germs, and Steel."
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adab085a408190af9fb40acca31a5f |
completed | March 8, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f3b7e5c48190a8fc84ae78d4bd66 |
completed | March 12, 2026, 5:11 p.m. |
Created at: March 8, 2026, 3:07 p.m.