Triple
T21266295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GIFT City |
E524136
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | GIFT City |
—
|
NE NERFINISHED |
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: GIFT City | Statement: [GIFT City, abbreviation, GIFT City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GIFT City Context triple: [GIFT City, abbreviation, GIFT City]
-
A.
GIFT City
chosen
GIFT City is India’s first operational smart business district and international financial services centre, developed as a global hub for finance and technology in Gujarat.
-
B.
Auto Nagar
Auto Nagar is an industrial area in Belagavi known primarily for its concentration of automobile-related workshops, small manufacturing units, and service industries.
-
C.
Connaught Place
Connaught Place is a prominent commercial and financial hub in central New Delhi, known for its colonial-era architecture, circular layout, and bustling markets, offices, and restaurants.
-
D.
DLF Mega Mall
DLF Mega Mall is a prominent multi-level shopping and entertainment complex in Gurugram, India, featuring retail stores, dining options, and a multiplex cinema.
-
E.
Desiro City
Desiro City is a family of modern electric multiple-unit passenger trains built by Siemens for high-capacity, high-frequency suburban and commuter rail services in the United Kingdom.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b5156d7881909bd4f83676590715 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e735eca49081908e4f13fcab717c41 |
completed | April 21, 2026, 8:31 a.m. |
Created at: April 16, 2026, 4 p.m.