Triple
T8777141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 311 |
E208612
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | “Amber” |
E458626
|
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: “Amber” | Statement: [311, notableWork, “Amber”]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: “Amber” Context triple: [311, notableWork, “Amber”]
-
A.
Amber
Amber is a historic town near Jaipur in Rajasthan, India, renowned for its hilltop Amber Fort and rich Rajput architectural heritage.
-
B.
Amber
Amber is a character from the film "Green Room," a tense horror-thriller about a punk band trapped in a remote venue controlled by violent neo-Nazis.
-
C.
Amber
chosen
Amber is a feminine given name derived from the English word for the fossilized tree resin, often associated with a warm, golden color.
-
D.
AMBER
AMBER is a fixed-target experiment at CERN designed to study hadron structure and strong interaction dynamics using high-energy secondary and tertiary beams from the SPS.
-
E.
Forever Amber
Forever Amber is a 1947 historical romantic drama film set in 17th-century England, adapted from Kathleen Winsor’s novel and known for its lavish production and controversial themes.
- 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_69ca835fbee88190bf625939bac48d7f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f51b3d48190b542a0423d3938e0 |
completed | March 31, 2026, 11:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf51d69af481909245ca327f36e9c2 |
completed | April 3, 2026, 5:36 a.m. |
Created at: March 30, 2026, 6:42 p.m.