Triple
T3380700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LAN Airlines (shares) |
E71174
|
entity |
| Predicate | investmentUse |
P45042
|
FINISHED |
| Object | exposure to Chilean airline industry |
—
|
LITERAL 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: exposure to Chilean airline industry | Statement: [LAN Airlines (shares), investmentUse, exposure to Chilean airline industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: investmentUse Context triple: [LAN Airlines (shares), investmentUse, exposure to Chilean airline industry]
-
A.
investment
Indicates a relationship where one party allocates resources (such as money, time, or effort) into something with the expectation of future benefit or return.
-
B.
investmentType
Indicates the specific category or nature of an investment associated with an entity or transaction.
-
C.
investmentScope
chosen
Indicates the range or boundaries of activities, assets, or sectors that an investment is intended or allowed to cover.
-
D.
vestmentStyle
Indicates the style or type of clothing or ceremonial garments associated with an entity.
-
E.
investsIn
Indicates that one entity allocates resources, typically money or capital, into another entity with the expectation of future returns or benefits.
- F. None of above.
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_69ad85a7f80c8190a05e43013f298942 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb5e7c7f48190afb78c311b424c93 |
completed | March 8, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69ada434bae48190a77ea37f9274ad8f |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:14 p.m.