Triple
T3465337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mariam-uz-Zamani |
E73122
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Amber |
E281917
|
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: [Mariam-uz-Zamani, birthPlace, Amber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amber Context triple: [Mariam-uz-Zamani, birthPlace, Amber]
-
A.
Amber
chosen
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.
Opal
Opal is a precious gemstone renowned for its vibrant play-of-color and is especially associated with major deposits in Australia.
-
D.
Zipolite
Zipolite is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its clothing-optional beach, bohemian vibe, and strong Pacific surf.
-
E.
Beryl
Beryl is a given name that can be used for people of any gender, historically more common as a female name in English-speaking countries.
- 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb0f2d3881908a5fa871341564ed |
completed | March 8, 2026, 6:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3612720308190b5a0d943a754883f |
completed | March 13, 2026, 12:58 a.m. |
Created at: March 8, 2026, 3:17 p.m.