Triple
T16937480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DTTA |
E410866
|
entity |
| Predicate | IATACode |
P418
|
FINISHED |
| Object | TUN |
E14014
|
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: TUN | Statement: [DTTA, IATACode, TUN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TUN Context triple: [DTTA, IATACode, TUN]
-
A.
TUN
chosen
TUN is the three-letter ISO 3166-1 alpha-3 country code assigned to Tunisia.
-
B.
TUNAIR
TUNAIR is the radio callsign used by Tunisair, the national flag carrier airline of Tunisia.
-
C.
Teredo
Teredo is a tunneling protocol that enables IPv6 connectivity for devices on IPv4 networks, particularly those behind NAT.
-
D.
TNET
TNET is the stock ticker symbol for Telenet Group, a Belgian telecommunications and entertainment services provider.
-
E.
Tunnel Log
Tunnel Log is a fallen giant sequoia in Sequoia National Park that has been hollowed to allow cars to drive through its trunk, making it a popular roadside attraction.
- 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_69d886c886688190967be07322597ac9 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cf2adf448190ab0dbebb3addbd80 |
completed | April 18, 2026, 6:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00cfe5210c8190873be6ccf2369b34 |
completed | May 10, 2026, 6:35 p.m. |
Created at: April 10, 2026, 5:30 a.m.